U.S. Department of the Interior

U.S. Geological Survey

Open-File Report 2014–1263

The Critical Role of Islands for Waterbird Breeding and 

Foraging Habitat in Managed Ponds of the 

South Bay Salt Pond Restoration Project, 

South San Francisco Bay, California
Cover: American avocet on a nest in San Francisco Bay, California. Photograph taken by Ken Phenicie in 2008 and used with permission.
The Critical Role of Islands for Waterbird Breeding and 

Foraging Habitat in Managed Ponds of the South Bay Salt 

Pond Restoration Project, South San Francisco Bay, 

California

By Joshua T. Ackerman, C. Alex Hartman, Mark P. Herzog, Lacy M. Smith, Stacy M. Moskal, 

Susan E.W. De La Cruz, Julie L. Yee, and John Y. Takekawa

Open-File Report 2014–1263

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2014 

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Suggested citation:

Ackerman, J.T., Hartman, C.A., Herzog, M.P., Smith, L.M., Moskal, S.M., De La Cruz, S.E.W., Yee, J.L., and

Takekawa, J.Y., 2014, The critical role of islands for waterbird breeding and foraging habitat in managed ponds of the 

South Bay Salt Pond Restoration Project, South San Francisco Bay, California: U.S. Geological Survey Open-File 

Report 2014-1263, 108 p., http://dx.doi.org/10.3133/ofr20141263. 

ISSN 2331-1258 (online)
iii

Contents

Executive Summary.................................................................................................................................................... 1

Nesting Waterbird Use of Islands............................................................................................................................ 1

Wintering Waterbird Use of Islands......................................................................................................................... 3

Introduction................................................................................................................................................................. 4

Study Objectives ..................................................................................................................................................... 6

Study Area.................................................................................................................................................................. 6

Using Historical Nesting Data to Model the Effects of Pond and Island Attributes That Influence Waterbird Nest 

Abundance and Nest Success.................................................................................................................................... 7

Methods .................................................................................................................................................................. 7

Salt Pond and Island Attributes ........................................................................................................................... 7

Historical Waterbird Nesting Data for San Francisco Bay ................................................................................... 8

Estimating Annual Average Nest Success for Each Pond and Nesting Island .................................................... 8

Statistical Analyses ............................................................................................................................................. 8

Model Selection ............................................................................................................................................... 9

Results and Discussion........................................................................................................................................... 9

Pond Scale Nest Abundance............................................................................................................................... 9

Pond Scale Nest Success ................................................................................................................................. 10

Island Scale Nest Abundance ........................................................................................................................... 11

Island Scale Nest Success................................................................................................................................ 11

Summary........................................................................................................................................................... 12

Using GPS Technology to Model Waterbird Nest-Site Selection at Island Patch and Microhabitat Scales .............. 15

Methods ................................................................................................................................................................ 15

Island Topography............................................................................................................................................. 15

Nest Microhabitat Topography and Vegetation ................................................................................................. 16

Statistical Analyses ........................................................................................................................................... 17

Nest-Site Selection at the Island Grid Scale .................................................................................................. 17

Nest-Site Selection at the Microhabitat Scale................................................................................................ 18

Model Selection ............................................................................................................................................. 18

Results and Discussion......................................................................................................................................... 19

Nest-Site Selection at the Island Grid Scale...................................................................................................... 19

Nest-Site Selection at the Microhabitat Scale.................................................................................................... 20

Summary........................................................................................................................................................... 22

Factors Influencing Wintering Waterbird Abundance on Created Islands in Pond SF2 ........................................... 25

Methods ................................................................................................................................................................ 25

Island-Scale Surveys......................................................................................................................................... 25

Island-Scale Environmental Data ...................................................................................................................... 25

Island-Scale Analysis ........................................................................................................................................ 26

Results and Discussion......................................................................................................................................... 27

Island-Scale Surveys......................................................................................................................................... 27

Summary........................................................................................................................................................... 27

Importance of Islands for Wintering Waterbirds Using Managed Ponds................................................................... 29

Methods ................................................................................................................................................................ 29

Pond and Grid-Scale Waterbird Surveys........................................................................................................... 29

Pond and Grid-Scale Environmental Data......................................................................................................... 29

Pond and Grid-Scale Analysis........................................................................................................................... 30
iv

Results and Discussion......................................................................................................................................... 32

Pond and Grid-Scale ......................................................................................................................................... 32

Pond Scale-Foraging......................................................................................................................................... 32

Pond Scale-Roosting......................................................................................................................................... 33

Grid Scale-Foraging .......................................................................................................................................... 34

Grid Scale-Roosting .......................................................................................................................................... 36

Summary........................................................................................................................................................... 37

Acknowledgments .................................................................................................................................................... 39

Unpublished References .......................................................................................................................................... 40

References Cited...................................................................................................................................................... 40

Appendix 1. AIC Tables for Pond and Grid Scale Analyses of the Importance of Islands for Foraging and 

Roosting Waterbirds ................................................................................................................................................. 86

Figures

Figure 1. Salt pond locations in South San Francisco Bay are grouped into several pond complexes within

the Don Edwards San Francisco Bay National Wildlife Refuge (Alviso, Moffet, Newark, Ravenswood) and

the Eden Landing Ecological Reserve..................................................................................................................... 45

Figure 2. Aerial view of Ravenswood Pond SF2 with constructed islands, South San Francisco

Bay, California ..........................................................................................................................................................46

Figure 3. Waterbird nest abundance was greatest within ponds very close (<1 kilometer [km]) or

far (4–5 km) from San Francisco Bay, California .....................................................................................................47

Figure 4. Waterbird nest success was greatest within ponds (a) very close (<1 kilometer) to 

San Francisco Bay, and (b) in ponds 1,200–1,700 hectares (ha) in size..................................................................48

Figure 5. Waterbird nest abundance was (a) greater on islands located closest (<1 kilometer) to San 

Francisco Bay, (b) greater on linear shaped islands (positive residuals) compared to rounded islands 

(negative residuals), and (c) increased as island distance to nearest surrounding pond levee increased................49

Figure 6. Waterbird nest success (a) decreased as island distance to San Francisco Bay increased, and (b) 

increased as island distance to surrounding pond levee increased to approximately 150 m, and decreased 

thereafte. ..................................................................................................................................................................50

Figure 7. American avocet probability of nesting on island grid cells in South San Francisco Bay in 2011 

(closed markers) and 2012 (open markers)..............................................................................................................51

Figure 8. Forster’s tern probability of nesting on island grid cells in South San Francisco Bay in 2011 

(closed markers) and 2012 (open markers)..............................................................................................................52

Figure 9. The proportion of each nest microhabitat grid cell and paired random site microhabitat grid cell in 

which vegetation was the dominant cover type among (a) American avocets and (b) Forster’s terns. .....................53

Figure 10. Total bird abundance observed at a) high tide and b) low tide on Pond SF2 islands by 

season for the survey years 2010–11, South San Francisco Bay, California ...........................................................54

Figure 11. Temporal trend in waterbird abundance on Pond SF2 for (A) low and (B) high tides across 

years, South San Francisco Bay, California. ............................................................................................................55

Figure 12. Mean bird abundance on Pond SF2 at high tide across all islands, South San 

Francisco Bay, California..........................................................................................................................................56

Figure 13. Abundance of foraging American avocets and medium shorebirds was greater in grids 

closer to islands, while diving duck abundance increased with increased distance from islands in South San 

Francisco Bay ponds, California ...............................................................................................................................57
v 

Tables

Table 1. Attributes of the 22 ponds modeled for waterbird nest abundance and nest success in South San 

Francisco Bay from historical nesting data collected in 2005–2013..........................................................................58

Table 2. Model selection results for factors affecting waterbird nest abundance n 22 ponds in South San 

Francisco Bay from historical nesting data collected in 2005–13..............................................................................59

Table 3. Model selection results for factors affecting waterbird nest success in 20 ponds in South San 

Francisco Bay from historical nesting data collected in 2005-13 ..............................................................................60

Table 4. Model selection results for factors affecting waterbird nest abundance on 100 islands in ponds in 

South San Francisco Bay from historical nesting data collected in 2005-13.............................................................61

Table 5. Model selection results for factors affecting waterbird nest success on 44 islands in ponds, from 

historic nesting data collected in 2005-13, South San Francisco Bay, California......................................................62

Table 6. Microhabitat characteristics examined in comparing American avocet and Forster’s tern nest sites to 

paired random sites in South San Francisco Bay, California, 2011 and 2012 ..........................................................63

Table 7. Number of American avocet nests and Forster’s tern nests with precise spatial data using real-time 

kinematic GPS, South San Francisco Bay, California, 2011 and 2012.....................................................................64

Table 8. Model selection results for logistic regression mixed models for the probability that an island 

grid cell was used for nesting by American avocets and Forster’s terns, South San Francisco Bay, 

California, 2011 and 2012.........................................................................................................................................66

Table 9. Model selection results for conditional logistic regression models evaluating differences between 

nest microhabitat and the microhabitat of a random paired site among American avocet and Forster’s tern 

nests, South San Francisco Bay, California, 2011 and 2012....................................................................................68

Table 10. Model averaged parameter estimates, standard errors (SE), lower (LCL) and upper (UCL) 

95 percent confidence limits, and standardized odds ratios for variables examined for differences between 

American avocet nest microhabitat and random paired site microhabitat and Forster’s tern nest microhabitat 

and random paired site microhabitat, South San Francisco Bay, California, 2011 and 2012. ..................................70

Table 11. Total abundance, relative percentage, and frequency of all birds observed in the units of Pond SF2, 

South San Francisco Bay, California, October through May 2010–12......................................................................71

Table 12. Guild relative abundance variance among islands in Pond SF2 (MANOVA, df =29), South 

San Francisco Bay, California ..................................................................................................................................73

Table 13. The presence or absence of guilds by island shape and size in Pond SF2 (binary logistic

regression, df =1), South San Francisco Bay, California ..........................................................................................74

Table 14. Number and area of islands in 22 ponds used to model foraging and roosting bird abundance 

using historic bird data collected between 2002–13, South San Francisco Bay, California ......................................75

Table 15. Common name and associated foraging guild of waterbird species observed in pondsSouth 

San Francisco Bay, California, October through April 2002–13................................................................................76

Table 16. Top ranking models at the pond scale for the abundance of foraging and roosting avian guilds 

and species in the former salt production ponds, South San Francisco Bay, California ...........................................77

Table 17. Parameter estimatesa of island variables from the top-ranked models of foraging bird abundance 

at the pond scaleb, South San Francisco Bay, California..........................................................................................78

Table 18. Parameter estimatesa of island variables from the top-ranked models of roosting bird abundance 

at the pond scaleb, South San Francisco Bay, California..........................................................................................79

Table 19. The influence of island variables on the foraging abundance of birds in ponds, South San 

Francisco Bay, California..........................................................................................................................................80
vi

Table 20. The influence of island variables on the roosting abundance of birds in ponds, South San

Francisco Bay, California.........................................................................................................................................81

Table 21. Top ranking models at the grid scale for the abundance of foraging and roosting birds in the

former salt production ponds, South San Francisco Bay, California.........................................................................82

Table 22. Parameter estimatesa of island variables from the top-ranked models of foraging bird abundance at the 

grid scale. ................................................................................................................................................................. 83

Table 23. Parameter estimatesa of island variables from the top-ranked models of roosting bird abundance at the 

grid scale. ................................................................................................................................................................. 84

Table 24. The influence of island variables on the foraging abundance of birds in grids..........................................85

Table 25. The influence of island variables on the roosting abundance of birds in grids..........................................85

Conversion Factors and Datums

Conversion Factors

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

meter (m) 1.094 yard (yd) 

Area

square meter (m2) 0.0002471 acre 

square meter (m2) 10.76 square foot (ft2)

hectare (ha) 2.471 acre

hectare (ha) 0.003861 square mile (mi2)

Datums

Vertical coordinate information is referenced to the North American Vertical Datum of 1988 (NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).
1 

The Critical Role of Islands for Waterbird Breeding and 

Foraging Habitat in Managed Ponds of the South Bay Salt 

Pond Restoration Project, South San Francisco Bay, 

California

By Joshua T. Ackerman, C. Alex Hartman, Mark P. Herzog, Lacy M. Smith, Stacy M. Moskal, Susan E. W. De La 

Cruz, Julie L. Yee, and John Y. Takekawa

Executive Summary

The South Bay Salt Pond Restoration Project aims to restore 50–90 percent of former salt 

evaporation ponds into tidal marsh in South San Francisco Bay, California. However, large numbers of 

waterbirds use these ponds annually as nesting and foraging habitat. Islands within ponds are 

particularly important habitat for nesting, foraging, and roosting waterbirds. To maintain current 

waterbird populations, the South Bay Salt Pond Restoration Project plans to create new islands within 

former salt ponds in South San Francisco Bay. In a series of studies, we investigated pond and 

individual island attributes that are most beneficial to nesting, foraging, and roosting waterbirds. 

Nesting Waterbird Use of Islands

We evaluated waterbird nesting on pond islands at multiple spatial scales. First, at the largest

spatial scale, we used historical nesting data (2005–13) to investigate how attributes of South Bay ponds 

influenced waterbird nest abundance and nest success among ponds. Pond attributes examined included 

total pond area, the number of islands within a pond, total island area, the ratio of island area to pond 

area, and distance to San Francisco Bay. Second, at the intermediate spatial scale, we used historical

nesting data (2005–13) to investigate how attributes of individual islands in ponds influenced waterbird 

nest abundance and nest success on islands. Island attributes examined included island area, island 

shape, island distance to San Francisco Bay, and island distance to nearest surrounding pond levee. 

Third, at the smallest spatial scale, we used highly precise Global Positioning System (GPS) technology 

to study the topography of nesting islands to investigate within-island attributes selected by nesting 

waterbirds (2011–12). At this small scale, we evaluated how elevation, distance to the water’s edge, 

slope, and aspect of island grids, as well as the number and distribution of other nesting birds, 

influenced waterbird use of island patches for nesting. Fourth, we also used highly precise GPS 

technology to measure attributes of individual nests (nest microhabitat), and by comparing them to 

attributes measured at random sites on the same island, we evaluated the environmental cues waterbirds 

use to select specific nest locations (2011–12). Nest microhabitat attributes included elevation, distance 

to the water’s edge, slope, aspect, and terrain ruggedness. 
2 

Results of the nesting studies identified several attributes of ponds, and islands therein, that most 

benefit nesting waterbirds in South San Francisco Bay. Based on these identified attributes, we suggest 

the following recipe for pond and island construction and management to improve waterbird nesting 

habitat:

1. Locate ponds and islands for nesting waterbirds near (<1 km) San Francisco Bay because these 

islands exhibited greater nest abundance and nest success than those at greater distances. 

2. Where possible, construct islands 100–200 m from the nearest surrounding pond levee because 

such islands exhibited greater nest abundance and nest success than those at closer and further 

distances. 

3. Construct nesting islands to be more linear in shape, rather than rounded. Although there was no 

difference in nest success between linear and rounded islands, linear islands exhibited as much 

as eight times more nests than rounded islands after accounting for all other island variables 

including size. However, although the recently constructed linear islands in Pond SF2 are more 

linear in shape than the recently constructed round islands in Pond SF2, the linear islands in 

Pond SF2 are actually much more rounded than the islands that support the greatest nest 

abundances. Therefore, it is recommended that islands constructed in the future be more linear 

than those recently constructed in Pond SF2 and Pond A16. Instead, we suggest mimicking the 

linear shape of islands in Pond A2W and the four historical islands at the southern end of Pond 

A16. 

4. Construct three to five nesting islands in multiple ponds, as opposed to constructing many 

islands in just a few ponds. 

5. Construct islands that are 0.05–0.10 ha in size, and given that birds preferred linear islands, we 

suggest islands that are approximately 50 m long by 10 m wide (0.05 ha) to 100 m long by 10 m 

wide (0.10 ha) with an understanding that island erosion may reduce island size over time and 

constructing islands slightly larger than the ideal size may improve the longevity of nesting 

islands. In comparison, the recently constructed “linear” islands in Pond SF2 and Pond A16 are 

70 m long by 25 m wide and likely are too “rounded” (too wide) for preferred use by waterbirds. 

6. Construct nesting islands with abundant area 0.5–1.5 m above the water surface. American 

avocet (Recurvirostra americana) preferred habitat was 0.5–1.0 m above the water surface, 

whereas Forster’s terns’ (Sterna forsteri) preference increased with elevation up to 1.3 m. This 

preference combined with the likelihood for erosion over time, indicates that islands be 

constructed with ample area that is 0.5–1.5 m above the water surface.

7. Construct nesting islands with abundant area within 10 m of the water’s edge. The probability of 

nesting peaked at approximately 7 m from the water’s edge for avocet nests, and 2 m from the 

water’s edge for tern nests. Similar to our results at the largest spatial scale, this result indicates 

that linear islands are more conducive to bird nesting than are rounded islands because, for a 

given island size, a linear shape allows for more area within 10 m of the water’s edge. 

8. Construct nesting islands with a mosaic of slopes ranging from flat to moderately steep (21

degrees). Avocets were more likely to nest in steep island grid cells whereas terns preferred flat 

grid cells. Thus, high topographic relief on islands is suggested, which is often at odds with the 

final smoothing of islands that is typically done during island construction.\ 
3 

9. Construct nesting islands with abundant area with south-facing slopes. Although avocets showed 

no preference for aspect of island grid cells, terns preferred to nest on south-facing slopes. To 

maximize the amount of area on linear islands with south-facing slopes, islands may be oriented 

west-to-east from end-to-end. 

10. Ensure nesting islands contain patches of short vegetation, ranging from 10 to 100 percent cover, 

as well as areas with little (<10 percent cover) or no cover; tall vegetation is not ideal. Avocets 

and terns were more likely to nest in microhabitats with vegetation (65 percent of avocet nests, 

76 percent of tern nests) than without it, and the most common species of vegetation were 

pickleweed (Sarcocornia pacifica) and alkali-heath (Frankenia salina). Yet, many avocet nests 

were in sparsely vegetated areas, suggesting that islands with complete vegetation cover would 

not be conducive to nesting by avocets. 

Wintering Waterbird Use of Islands

To evaluate the influence of islands on roosting and foraging waterbirds during winter, we 

conducted studies at several spatial scales. First, at the smallest spatial scale of the island, we focused on 

managed Pond SF2, and the 30 islands created for nesting and roosting birds. We conducted bi-weekly 

high and low tide bird surveys in the autumn, winter, and spring, and used a geographic information 

system to determine island spatial characteristics, including shape, size, and perimeter. Survey data were 

combined with physical pond and island parameters, and used to model total abundance and relative use 

of islands. Second, at an intermediate spatial scale, we used our historical waterbird dataset (2002–13) 

to assess the influence of islands on waterbird use of 250-m by 250-m grid cells within ponds for 

roosting and foraging. We considered the number of islands and island area within grids, the ratio of 

island area to grid area, and the distance from the center of the grid to the nearest island. Third, at the 

largest spatial scale, we used our historical waterbird dataset to assess the abundance and distribution of 

seven guilds and five species of birds across ponds with and without islands, relative to the number of 

islands, total island area, and island-area to pond-area ratio.

From the results of our island, grid, and pond scale analyses, we documented the importance of 

islands to wintering waterbirds, and identified island characteristics that influenced wintering waterbird 

abundance. Based on these characteristics, we make the following conclusions on the influence of 

islands on wintering waterbird abundance:

1. Waterbird abundance was greater in areas with islands than in areas without islands. In Pond 

SF2, waterbird densities were greatest on and near islands, and lower in open water, pond 

bottom, and levee habitats. Among all ponds with islands, abundance of most birds was greater 

in grid cells with islands than grid cells without islands, and island presence only negatively 

influenced the abundance of foraging eared grebes (Podiceps nigricollis). 

2. Islands provide important roosting habitat at high tide, when mudflat habitats are tidally 

inundated with water. In Pond SF2, islands were most heavily used by small shorebirds at high 

tide, particularly in the spring. 

3. Island size and shape influences waterbird abundance. In Pond SF2, small shorebirds were more 

likely to use round islands than linear islands at high tide during spring. During low tide in the 

winter, dabbling ducks were more likely to use small islands than large islands, and wading birds 

(herons) and piscivores were more likely to use linear islands than round islands. Gulls were 

more likely to use large, linear islands than round islands. 
4 

4. Ponds and grid cells with islands had a greater abundance of foraging American avocets, blacknecked stilts (Himantopus mexicanus), Forster’s terns, western sandpipers (Calidris mauri), 

dabbling ducks, diving ducks, gulls, herons, piscivores, eared grebes, and small and medium 

shorebirds, than those without islands. Across all ponds, abundance of Forster’s terns, gulls, and 

piscivores increased with increasing total island area, and American avocet abundance was 

greater in ponds with more islands. In ponds with islands, black-necked stilt and piscivore 

abundance increased with increasing island-area-to-pond-area ratio, and the abundance of 

Forster’s terns and small shorebirds increased with total island area.

5. Ponds and grid cells with islands had a greater abundance of roosting American avocets, blacknecked stilts, Forster's terns, western sandpipers, dabbling ducks, diving ducks, gulls, herons, 

piscivores, and small and medium shorebirds. Across all ponds, abundance of all waterbirds 

except small shorebirds increased with increasing island-area to pond-area ratio. In ponds with

islands, the abundance of diving ducks increased with island area, and the abundance of gulls 

was greater in areas closer to islands. 

6. Scattering islands across a pond, rather than clustering them together, may increase the preferred 

foraging area of many birds. The abundance of foraging American avocets, gulls, and medium 

shorebirds was greatest closer to islands, although diving ducks were most abundant farther from 

islands. 

Introduction

San Francisco Bay is the largest estuary on the West Coast of North America, but nearly 80 

percent of its tidal marshes and 40 percent of its tidal flats have been lost over the past two centuries due 

to urban development, agriculture, and salt production (Goals Project, 1999). In particular, about 14,000 

ha (35,000 acres) of artificial salt evaporation ponds were constructed within the former baylands 

(Goals Project, 1999). Recently, more than 6,110 ha (15,100 acres) of former salt ponds have been 

transferred to government ownership, the majority of which are now a part of the U.S. Fish and Wildlife 

Service Don Edwards San Francisco Bay National Wildlife Refuge (hereafter Don Edwards Refuge). 

The South Bay Salt Pond (SBSP) Restoration Project is implementing a large-scale plan to convert 50–

90 percent of these former salt ponds into tidal and managed marsh habitats within the next 50 years 

(Goals Project, 1999; Steere and Schaefer, 2001; Siegel and Bachand, 2002; Life Science!, 2003). 

The San Francisco Bay is a designated site of hemispheric importance to shorebirds and annually 

supports more than 1 million waterbirds (Page and others, 1999; Morrison, 2001; Stenzel and others, 

2002). The bay supports more than 325,000 shorebirds in autumn, 225,000 in winter, and as many as

932,000 during spring migration (Stenzel and others, 2002). Shorebird abundances during peak spring 

migration have exceeded 200,000 shorebirds in a single salt pond (Stenzel and Page, 1988). Western 

sandpiper (Calidris mauri) is the most abundant shorebird species, with populations exceeding 100,000 

in the autumn and winter, and peaking at more than 500,000 in the spring (Stenzel and others, 2002). 

San Francisco Bay also is important for diving ducks, supporting 44 percent of the wintering population 

observed in the Lower Pacific Flyway (Richmond and others, 2014). The Bay is a particularly important 

area for wintering greater and lesser scaup (Aythya marila and A. affinis) and canvasback (A.

valisineria), supporting 60 and 51 percent of their respective Lower Pacific Flyway abundance

(Richmond and others, 2014). During migration and over winter, birds rely on a mosaic of inter- and 

subtidal shoal and pond habitats for foraging and roosting (Brand and others, 2014; Rocha and others, 

unpublished). Although the restoration of former salt ponds to tidal marsh will increase habitat for many 

animals, including the endangered California clapper rail (Rallus longirostris obsoletus) and salt marsh 
5 

harvest mouse (Reithrodontomys raviventris raviventris), it also will reduce the overall pond habitats 

available for wintering, migratory, and breeding waterbirds. A goal of the SBSP Restoration Project is 

to maintain current waterbird populations. Towards this goal, the first phase of the SBSP Restoration 

Project has reconfigured and enhanced two existing ponds (Ponds A16 and SF2) by constructing islands 

to increase roosting and foraging opportunities and to provide waterbird nesting habitat (Trulio and 

others, 2007). Former salt ponds provide critical habitat for wintering and breeding waterbirds. For 

example, radio-marked Forster’s terns (Sterna forsteri; hereafter terns) strongly selected ponds, 

especially low salinity ponds, as foraging habitat during the pre-breeding (Ackerman and others, 2008) 

and breeding seasons (Bluso-Demers and others, unpublished; Ackerman and others, 2009). Similarly, 

western sandpipers selected salt ponds and avoided tidal marsh habitats during the winter (Warnock and 

Takekawa, 1995; Warnock and others, 2002). Thirty-six percent of the Lower Pacific Flyway 

population of ruddy ducks (Oxyura jamaicensis) use San Francisco Bay and 77 percent of those use the 

salt ponds in South San Francisco Bay (Richmond and others, 2014). American avocets (Recurvirostra 

americana; hereafter avocets) and black-necked stilts (Himantopus mexicanus; hereafter stilts) used 

pond habitats more than any other habitat in the estuary (Ackerman and others, 2007; Hickey and 

others, 2007). Dry areas of former salt ponds also provide foraging and nesting habitat for the 

endangered western snowy plover (Charadrius alexandrinus nivosus; Robinson and others, 2007). 

In addition to ponds providing preferred foraging habitat, islands and levees are used extensively 

by roosting waterbirds (Goals Project, 1999; Takekawa and others, 2000; Colwell and others, 2003; 

Conklin and others, 2007). Western sandpipers selected pond levees as roosting sites, especially during 

high tides, throughout the winter (Warnock and Takekawa, 1995). Additionally, pond islands and levees 

play a critical role for fledging chicks. For instance, postfledging terns were closer to pond levees than 

would be predicted by chance, indicating that they foraged near levees and used levees as roosting sites 

as they departed their natal colony (Ackerman and others, 2009). 

In addition to providing roosting and foraging habitats, former salt ponds support nesting habitat 

for locally breeding waterbirds, especially terns and avocets. Approximately 30 percent of the breeding 

population of Forster’s terns on the Pacific Coast nests in the San Francisco Bay (McNicholl and others, 

2001; Strong and others, 2004), and the islands within ponds currently provide nesting habitat for 80 

percent of those terns (Strong and others, 2004). Thus, the project area currently accounts for about onequarter of the nesting habitat on the Pacific Coast. Similarly, the estuary is the largest breeding area for 

avocets along the Pacific Coast (Stenzel and others, 2002; Rintoul and others, 2003), and 75 percent of 

breeding avocets in the South Bay nest on islands within ponds (Ackerman and others, 2013). 

As the largest tidal wetland restoration project on the West Coast of the United States proceeds, 

there are several key uncertainties about how to enhance and manage existing ponds to maximize 

waterbird foraging and nesting opportunities as pond habitats are reduced. Pond islands and levees are 

expected to continue to support high densities of roosting birds, and the experimental addition of islands 

to ponds is expected to enhance this effect (Trulio and others, 2007). Herein, we address these key 

uncertainties to help direct future management actions to maximize waterbird habitat in the remaining 

ponds. 
6 

Study Objectives

Whereas our broad objectives were to quantify the benefits that islands within ponds of South 

San Francisco Bay provide to waterbirds, our more specific objectives were as follows: 

1. Assess how pond and island attributes affect waterbird nest abundance and nest success. 

2. Assess how the specific structure (topography and vegetation) of islands influence nest-site

selection, nest densities, and reproductive success of avocets and terns.

3. Evaluate factors influencing the variation in numbers of waterbirds roosting and foraging near 

the newly created islands in Pond SF2. 

4. Using pond complex-wide surveys, evaluate whether waterbird diversity and abundance at a 

broader scale are influenced by island habitat and water depth within ponds. 

Study Area

The primary study area was within former salt ponds of the Don Edwards Refuge and Eden 

Landing Ecological Reserve (fig. 1). Our evaluation of pond and island attributes on waterbird nest 

abundance and nest success was conducted on 100 nesting islands in 22 ponds (table 1, fig. 1). Our 

evaluation of island morphometry and vegetation structure on avocet and tern nest-site selection was 

conducted on 28 islands within 10 ponds (table 7, fig. 1). 

Our study to evaluate factors that influence roosting and foraging at the large, or pond, scale 

included former salt production ponds within the SBSP Restoration Project in South San Francisco Bay, 

California (fig. 1). Project ponds were divided into three regional complexes: Eden Landing (managed 

by the California Department of Fish and Wildlife as part of the Eden Landing Ecological Reserve), 

Ravenswood, and Alviso (both managed by the U.S. Fish and Wildlife Service as part of the Don 

Edwards Refuge). Ponds mostly consisted of moderate to deep water ponds managed with water control 

structures to increase circulation with bay water, and shallow water ponds that collected rainwater 

during the winter and dried during the summer. 

Our study to evaluate factors that influence roosting and foraging at the island-scale

(intermediate) focused on former salt Pond SF2 in Ravenswood (fig. 2). Pond SF2 (lat 37°29’N., long 

122°07’W.) is a 57-ha impoundment bordered by the municipalities of East Palo Alto and Menlo Park 

to the west and south, U.S. Highway 84 to the north, and San Francisco Bay to the east. In 2009–10, the 

U.S. Fish and Wildlife Service created 30 islands ranging in size from 1,439 to 2,363 m2. Internal levees 

were constructed to divide the pond into two experimental units with islands. Unit 1 (23 ha) contained 8 

islands and was closest to the Bay, whereas Unit 2 (34 ha) contained 22 islands. Water control structures 

were placed along the Bay-front levee and weir boxes were installed into internal levees to allow for 

water level manipulation. All islands had a north facing slope that provided protection from northwest 

winds, the typical wind direction for the region. One-half of the islands were rounded—falcate-curved 

shapes with a low island-edge to island-area ratio; whereas the others were linear—long and rectangular 

with a saw-tooth south edge providing a high island-edge to island-area ratio.
7 

Using Historical Nesting Data to Model the Effects of Pond and Island Attributes 

That Influence Waterbird Nest Abundance and Nest Success

Methods

Salt Pond and Island Attributes

We used historical nesting data to evaluate waterbird nesting across entire ponds (pond scale) 

and on individual nesting islands (island scale). At the pond scale of analysis, we investigated whether 

the amount and distribution of island habitat within a pond influenced waterbird nest abundance or nest 

success by evaluating the effects of the number of islands in a pond, total island area, and the ratio of 

island area to pond area. We also evaluated the effects of total pond area and pond distance to San 

Francisco Bay, as these attributes may affect nest abundance and nest success through corresponding 

differences in food availability and predator densities. Pond areas were calculated using pond polygon 

shapefiles digitized from 2005 National Agricultural Imagery Program (NAIP) imagery for South San 

Francisco Bay, in ArcMapTM 10.2 (Environmental Research Systems Institute [ESRI], Redlands, 

California). Island areas were calculated from island perimeters derived using one of two methods. For 

29 islands, we used real-time kinematic Global Positioning System (GPS; Leica® Smart Rover 

GPS1200, Leica Geosystems Inc., Atlanta, Georgia) to trace the island perimeter at the water’s edge.

Universal Transverse Mercator (UTM) coordinates (accuracy: 3.75 cm) were collected at 1-second 

intervals while the perimeter of each nesting island was traversed and the water’s edge marked using the 

GPS unit. Island perimeters were traced using real-time kinematic GPS in April and May 2011. For the 

remaining 125 islands in the study ponds, we digitized island perimeter polygon shapefiles from 2011 

U.S. Geological Survey (USGS) high-resolution orthoimagery of the San Francisco Bay area 

(resolution: 0.3 m), in ArcMap 10.2. These data were collected in April 2011, thereby matching the 

period of data collection of island perimeters using real-time kinematic GPS. Total island area in each 

pond was calculated by summing the island areas of all islands within a given pond. Pond distance to 

San Francisco Bay was calculated as the minimum distance from the pond’s edge to San Francisco Bay 

using the Near Geoprocessing Tool in ArcMap10.2. 

Next, at the island scale of analysis, we evaluated the effects of island area, island shape, island 

distance to San Francisco Bay, and island distance to nearest surrounding pond levee on waterbird nest 

abundance and nest success among individual islands. We included the distance to nearest surrounding 

pond levee as this may influence island accessibility by land-based egg and chick predators, as well as 

the potential for human disturbance. The area of each island was calculated from the island polygon 

shapefiles previously described. Island shape was quantified using the residuals of a general linear 

regression of island perimeter length on island area such that positive residual values denoted more 

linearly-shaped islands while negative residual values denoted more rounded islands. Unlike simple 

perimeter-to-area ratios, residuals of the regression allowed for a measure of island shape that was 

independent of island size. Lastly, we calculated the distance of each island edge to the nearest pond 

levee and to San Francisco Bay using the Near Geoprocessing Tool in ArcMap10.2. 
8 

Historical Waterbird Nesting Data for San Francisco Bay

The USGS Western Ecological Research Center, Dixon Field Station has been studying 

waterbird breeding ecology in South San Francisco Bay since 2005 and has collected and archived 

detailed records from more than 15,000 individual waterbird nests into a computer database. The large 

number of nest records, collected over almost a decade, provided us with a robust dataset for evaluating 

waterbird nesting preferences and nest success. We compiled historical USGS waterbird nesting data 

from ponds of the Don Edwards Refuge and Eden Landing Ecological Reserve over the years 2005–13. 

We included only nests that were located on islands within ponds (levee and marsh nesting birds were 

removed from the dataset). Furthermore, we only included data from an island when all nests on that 

island were monitored, thereby providing us with an accurate island-level estimate of nest abundance. 

Similarly, for the pond scale analysis, we only included data from ponds for which all islands on the 

pond were monitored, thereby providing us with an accurate nest abundance estimate among all islands 

within a given pond. We focused our analyses on the three most numerous species—Forster’s tern, 

American avocet, and black-necked stilt. 

Estimating Annual Average Nest Success for Each Pond and Nesting Island

Throughout the nesting season (April through August), we visited nesting pond islands weekly 

to monitor waterbird nesting activity. We uniquely marked each newly initiated nest, and recorded UTM 

coordinates of each nest (Garmin GPSMAP 76, Garmin International Inc., Olathe, Kansas). At each 

weekly nest visit, we floated eggs to determine embryo age (Ackerman and Eagles-Smith, 2010), 

recorded clutch size, determined overall nest fate (hatched, failed, abandoned, or depredated), and 

determined the fate of each individual egg (hatched, failed-to-hatch, abandoned, or depredated). 

We estimated daily nest survival rates based on weekly nest visits using logistic exposure 

models (Shaffer, 2004). A nest was considered to have survived an interval if the clutch was still 

completely or partially intact, embryo development had progressed, and there were no signs of nest 

abandonment (such as cold eggs). A nest was considered successful if 1 or more eggs successfully 

hatched. A nest was considered unsuccessful if it was destroyed or abandoned. Exposure days were 

calculated as the number of days between nest visits, except when a final nest fate occurred between 

visits (hatched, depredated, or abandoned). For hatched nests, we calculated exposure days for the final 

interval based on the expected hatch date (Ackerman and Eagles-Smith, 2010). For depredated nests, we 

calculated exposure days for the final interval as the mid-point between nest visits. For abandoned nests, 

we calculated exposure days for the final interval as the difference between the developmental age of 

the eggs when the nest was abandoned (estimated by egg flotation) and the developmental age of the 

eggs when the nest was last visited. Daily nest survival estimates were estimated separately for each 

year, species, pond, and island with nest age as the single covariate in the model. Nest success was 

calculated as the model averaged product of daily nest survival over the approximate 27 day incubation 

period (Ackerman and others, 2013). 

Statistical Analyses

For the pond-scale analysis, we used repeated measures linear mixed models (PROC MIXED, 

SAS/STAT® software, release 9.4, SAS Institute, Cary, North Carolina) to evaluate the effects of 

species, year, total pond area, the number of nesting islands within a pond, and total island area on 

waterbird nest abundance and nest success within ponds. We used apparent nest abundance, or the 

number of nests observed during weekly colony visits. Often nest abundance studies use adjusted nest 

abundance estimates that account for nests that failed before they could be found. However, because we 
9 

visited colonies weekly, and islands represented a finite area that could be searched systematically, we

determined that adjustments to apparent nest abundance were unnecessary. In analyzing pond nest 

abundance in ponds, we included only data from ponds for which all nesting islands were monitored in a 

given year. Additionally, in analyzing nest success, we only included data from ponds for which 10 or 

more nests of a given species were monitored in a given year, so as to provide us with a reasonably 

accurate estimate of nest success. We built a relatively balanced set of candidate models based on all 

combinations of the class variables species and year, and linear and quadratic terms for total pond area, 

the number of islands within a pond, total island area, the ratio of island area to pond area, and distance 

to San Francisco Bay, plus a null model (940 total models). Nest abundance values were not normally 

distributed so we used a natural log data transformation to meet the assumption of normality. 

For the island-scale analysis, we again used repeated measures linear mixed models (PROC 

MIXED, SAS/STAT) to evaluate the effects of island area, island shape, island distance to San 

Francisco Bay, and island distance to nearest pond levee on waterbird nest abundance and nest success 

on islands. We included only data from islands for which all nests were monitored, and in analyzing 

nest success, we only included data from islands for which 10 or more nests of a given species were 

monitored in a given year. We built a relatively balanced set of candidate models based on all 

combinations of the class variables species and year, and linear and quadratic terms for island area; 

island shape (residuals of island perimeter to area regression); island distance to San Francisco Bay; and 

island distance to nearest pond levee, plus a null model (324 total models). For all island-scale models, 

we included the pond in which the island was located as a random effect. Nest abundance values were 

not normally distributed so we used a natural log data transformation to meet the assumption of 

normality. 

Model Selection

For the pond and island scales of analysis, we ranked models using an information-theoretic 

approach and second-order Akaike Information Criterion (AICc; Burnham and Anderson, 2002). The 

model with the lowest AICc score was considered to be the most parsimonious, and we used the 

difference in AICc values (ΔAICc) between the best model and each other model in the candidate set to 

assign model rank. We considered models with a ΔAICc score less than or equal to 2.0 to be competitive

and calculated the beta parameters of the variables by model-averaging all models in the candidate set.

The weight of evidence for each model was determined using Akaike model weights (wi), defined as the 

relative likelihood of a model given all models in the candidate set. We used evidence ratios, or the ratio 

of the Akaike model weight of one model to the Akaike model weight of another model, to assess the 

relative weight of support between models. 

Results and Discussion

Pond Scale Nest Abundance

We examined historical nest abundance on a total of 22 ponds (table 1). A total of 9,404 avocet, 

stilt, and tern nests were monitored on islands in these ponds between 2005 and 2013. Not all 22 ponds 

were used by nesting avocets, stilt, and terns in all years. Moreover, in some years, ponds used for 

nesting were not monitored for nest abundance. Therefore, for each pond, we included only data from 

years when one or more island nests were initiated, and all island nests were monitored. 
10

The most parsimonious model describing waterbird nest abundance on ponds in South San 

Francisco Bay included the effects of species and a quadratic term for distance to bay; had an Akaike 

weight of 0.11; and was 1.08 times more likely than the next best model (table 2) to explain nest 

abundance. Five other models were competitive (∆AICc < 2.0), and included the effects of species (all 

six models), linear terms for the number of islands (two models) and total pond area (one model), and 

quadratic terms for distance to bay (three models), the ratio of island area to pond area (one model), and 

the number of islands (one model). However, 95-percent confidence intervals of the model-averaged 

parameter estimates for the number of islands, total pond area, and the ratio of island area to pond area 

all overlapped zero, suggesting they had little to no effect on the number of waterbird nests initiated 

within ponds. To further evaluate the importance of the variables in the best model, we compared 

evidence ratios between the best model and an identical model but with one of the variables removed. 

Using evidence ratios, we determined that the best model was 2.98×1014 times more likely than a 

similar model but without species, and 2.76 times more likely than a similar model but without distance 

to Bay. Predictions from model-averaged parameter estimates exhibited a U-shaped pattern, in which 

nest abundance decreased as a pond’s distance to San Francisco Bay increased to approximately 2 km, 

and thereafter nest abundance increased as distance to the Bay increased, with ponds 4–5 km from the 

Bay exhibiting the greatest nest abundance (fig. 3). 

Pond Scale Nest Success

We examined historical nest success using data from 9,119 nests on a total of 20 ponds (table 1).

Not all 20 ponds were used by nesting avocets, stilt, and terns in all years. Moreover, in some years, 

ponds used for nesting were not monitored for nest abundance, or nest abundance in a given year did not 

meet the 10 nest threshold we imposed for our nest success analysis. Therefore, for each pond, we 

included only data from years when 10 or more nests were monitored. 

The most parsimonious model describing waterbird nest success within ponds in South San 

Francisco Bay included a linear term for the number of islands in a pond and quadratic terms for 

distance to bay and total pond area; had an Akaike weight of 0.21; and was 1.06 times more likely than 

the next best model (table 3). Two other models were competitive (∆AICc < 2.0), one with a quadratic 

terms for distance to Bay and total island area, and one the same as the best model but without a linear 

term for number of islands in the pond. Using evidence ratios, we determined that the best model was 

3.2×104 times more likely than a similar model but without distance to bay; 21 times more likely than a 

similar model but without total pond area; and only 1.5 times more likely than a similar model but 

without the number of islands within a pond. Furthermore, 95-percent confidence intervals of the 

model-averaged parameter estimates for the effect of the number of islands in a pond overlapped zero, 

suggesting it had little to no effect on waterbird nest success on pond islands. Predictions from modelaveraged parameter estimates showed that nest success was slightly greater on islands in ponds very 

close (<1 km) to San Francisco Bay (fig. 4a) and that nest success was greatest in ponds that were 

1,200–1,700 ha in size (43–46 percent), and lower within smaller (33–37 percent for 200 ha) and larger 

(26–31 percent for 2,700 ha) ponds (fig. 4b). 
11

Island Scale Nest Abundance

We examined nest abundance on a total of 100 individual islands within 22 ponds (table 1). Not 

all 100 islands were used for nesting by avocets, terns, or stilts in all years. Moreover, in some years, 

islands used for nesting were not monitored for nest abundance. Therefore, for each island, we included 

only data from years when one or more nests were initiated, and all nests were monitored. 

The most parsimonious model describing waterbird nest abundance on islands in South San 

Francisco Bay included the effects of species, a linear term for distance to nearest pond levee, and 

quadratic terms for distance to Bay, and the residual of island perimeter length to island area; had an 

Akaike weight of 0.48; and was 3.10 times more likely than the next best model (table 4). No other 

model was competitive (∆AICc < 2.0). Using evidence ratios, we determined that the best model was 

9.99×1035 times more likely than a similar model but without species, 9.47×104 times more likely than a 

similar model but without the residual of perimeter to area, 614 times more likely than a similar model 

but without distance to Bay, and 13 times more likely than a similar model but without distance to 

nearest pond levee. Predictions from model-averaged parameter estimates exhibited a U-shaped pattern, 

in which waterbird nest abundance decreased as island distance to San Francisco Bay increased from 0 

km to about 3 km, after which nest abundance increased slightly as island distance to San Francisco Bay 

increased to 5 km from the Bay (fig. 5a). Moreover, nest abundance was greater on more linearlyshaped islands (positive residuals for perimeter to area regression; fig. 5b). Lastly, waterbird nest 

abundance increased as island distance to surrounding pond levee increased, such that approximately 

three times as many nests of each species were predicted on islands 300 m from the levee than islands 

next to the levee (fig. 5c). 

Island Scale Nest Success

We examined historical nest success using data from 8,156 nests on a total of 44 islands within 

19 ponds. The most parsimonious model describing waterbird nest success on islands in South San 

Francisco Bay included the effects of year, a linear term for distance to nearest levee, and a quadratic 

term for distance to Bay; had an Akaike weight of 0.31; and was 2.25 times more likely than the next 

best model (table 5). A model similar to the best model but including island area also was competitive 

(∆AICc =1.63). Using evidence ratios, we determined that the best model was 7.64×105 times more 

likely than a similar model but without year, 21 times more likely than a similar model but without 

distance to Bay, and 16 times more likely than a similar model but without distance to nearest levee. 

Predictions from model-averaged parameter estimates indicated that waterbird nest success was slightly 

greater on islands located very close (< 1 km) to San Francisco Bay (40–47 percent), compared to 

islands 5 km from the Bay (37–40 percent, fig. 6a). In addition, nest success increased slightly from 31–

35 percent on islands adjacent to a pond levee to 35–39 percent on islands 130 m from the pond levee, 

after which nest success decreased as island distance to levee increased (fig. 6b). 
12

Summary

Island nesting habitat is extremely important to waterbirds in South San Francisco Bay. Between 

2005 and 2013, we monitored 15,066 waterbird nests (6,003 avocet, 1,433 stilt, and 7,630 tern) in South 

San Francisco Bay. Of these, 73 percent of avocet, 21 percent of stilt, and 96 percent of tern nests were 

observed on island habitats. Terns were the most numerous island-nesting species (other than California 

gulls), and accounted for 65 percent (6,134) of the 9,404 nests on islands monitored within the 22 study 

ponds between 2005 and 2013. Avocets were the second most numerous island-nesting species, and 

accounted for 32 percent (3,022) of nests on islands. Stilts, in contrast, accounted for only 3 percent

(248) of nests on islands, and prefer to nest in more vegetated marshes, particularly New Chicago Marsh 

which accounts for approximately 63 percent of all stilt nests.

Distance to San Francisco Bay was an important predictor of waterbird nest abundance across 

South San Francisco Bay ponds and on individual islands. At the pond scale of analysis, and at mean 

values for all other variables, ponds 100 m from San Francisco Bay were predicted to have 12.1 avocet, 

2.6 stilt, and 32.7 tern nests per year (fig. 3). Indeed, several of the largest avocet and tern breeding 

colonies have occurred within ponds less than 100 m from San Francisco Bay (Ponds A1, A2W, AB1, 

AB2, E2, N4/N5, N4AB, R1, and SF2; table 1). Predicted pond nest abundance decreased to 8.3 avocet, 

1.7 stilt, and 22.3 tern nests per year in ponds located 2 km from the Bay. Similarly, at the island scale 

of analysis, predicted nest abundance was 13.7 avocet, 4.1 stilt, and 44.1 tern nests per year on islands 

100 m from San Francisco Bay, but only 3.9 avocet, 1.2 stilt, and 12.6 tern nests on islands 3.5 km from 

the Bay (fig. 5a). Breeding avocets and stilts primarily forage in ponds, tidal marshes, tidal flats, and 

managed marshes (Ackerman and others, 2007). Thus, nesting close to these preferred foraging habitats 

may be more attractive, prompting greater nest abundance on near-Bay islands and ponds. 

Model-predicted nest abundance also was high within ponds farther from San Francisco Bay 

(fig. 3), with 25.2 avocet, 5.3 stilt, and 68.0 tern nests predicted in ponds 5 km from the Bay. This result 

is due mostly to the influence of two ponds, Ponds A16 and A8, both of which are more than 3.7 km 

from San Francisco Bay and historically have had large numbers of nesting waterbirds. However, both 

of these ponds have undergone dramatic changes since 2013. In 2013, Pond A16 was enhanced to 

increase nesting and foraging value for waterbirds. Sixteen new islands were constructed, increasing the 

total number of nesting islands in Pond A16 from 5 to 20. The initial response by waterbirds has been 

positive, with 81 avocet nests monitored in 2013, and 68 avocet nests monitored in 2014. However, only 

a single tern nest (in 2013) has been initiated in Pond A16 in the past two years, which is a drastic 

reduction from the yearly average of approximately 150 tern nests in Pond A16 during 2005–11. 

Continued monitoring is necessary to evaluate the waterbird response to the Pond A16 enhancement, 

and to determine if this pond returns to, or even exceeds, the high waterbird nest abundance totals 

historically observed. In contrast, Pond A8 has lost almost all of its island nesting habitat. In 2013, the 

last year of this study, only 3 avocet nests were monitored in Pond A8, down from a yearly average of 

173 avocet nests during 2005–11. In 2014, only 8 avocet nests were observed throughout Pond A8. 

Changing pond water management associated with the opening of the Pond A8 notch, has caused all 

island habitat in Pond A8 to become inundated with water, thereby removing virtually all waterbird 

nesting habitat. Given the historical importance of Pond A8, particularly to nesting avocets and terns, 

future pond enhancements that effectively replace this lost nesting habitat will be critical to maintaining 

breeding waterbird numbers in South San Francisco Bay.
13

At the island scale of analysis, island shape and island distance to surrounding pond levee also 

were important in predicting waterbird nest abundance. Waterbird nest abundance was greatest on 

linearly-shaped islands relative to rounded islands (fig. 5b). At mean values for all other variables, 

waterbird nest abundance was predicted to be almost eight times greater on linear islands similar in 

shape to the southernmost island in Pond A2W (residual of island perimeter to island area = 207.4), than 

on more rounded islands similar in shape to the round islands in Pond SF2 (residual of island perimeter 

to island area approximately = -100). The southernmost island in Pond A2W is approximately 140 m 

long by 6.5 m wide (at its widest point), whereas, the round islands of Pond SF2 have an approximate 

diameter of 50 m. However, even more highly linearized islands, such as those similar in shape to the 

middle two historical islands at the south end of Pond A16 (residual of island perimeter to island area 

approximately = 330–350), although still exhibiting greater nest abundance than rounded islands, 

showed a decrease in nest abundance relative to islands similar in shape to the southernmost island in 

Pond A2W. Island 15 (second island from the west at the southern end of Pond A16) is approximately 

190 m long by 7 m wide. Linearly shaped islands improved nest abundance relative to rounded islands 

up to a point at which additional island size did not increase nest abundance. 

Nest abundance was greater on islands farther from surrounding pond levees (fig. 5c), such that 

at mean values for all other variables, predicted waterbird nest abundance on islands 300 m from the 

nearest surrounding pond levee was almost three times that of islands only 10 m from the nearest 

surrounding pond levee. In a previous study on nest predation in South San Francisco Bay, 71 percent of 

identifiable predations of avocet and stilt nests were caused by mammals, including raccoon (Procyon 

lotor), striped skunk (Mephitis mephitis), and gray fox (Urocyon cinereoargenteus; Herring and others, 

2011). Islands farther from pond levees may be more attractive to nesting waterbirds as birds may 

perceive them to be more effective at limiting access by mammalian predators and disturbance by 

humans. However, few islands in ponds of South San Francisco Bay are more than 200 m from the 

nearest surrounding pond levee (only three islands in Pond A7 and seven islands in Pond SF2). Thus, 

considering nest abundance alone, creating nesting islands at distances of 200 or more meters from 

levees would be ideal. However, the effect of island proximity to surrounding pond levee on nest 

success also could be considered (see below).

As with waterbird nest abundance, distance to San Francisco Bay was an important predictor of 

waterbird nest success within South San Francisco Bay ponds and on individual islands. At both the 

pond and island scales of analysis, model-predicted average nest success decreased with distance away 

from San Francisco Bay (figs. 4a and 6a). At mean values for all other variables, nest success within 

ponds 100 m from San Francisco Bay was predicted to be 50 percent for avocets, 51 percent for stilts, 

and 52 percent for terns. Nest success dropped to 40 percent for avocets, 42 percent for stilts, and 43 

percent for terns within ponds 3 km from the Bay, before increasing to 45 percent for avocets, 46 

percent for stilts, and 47 percent for terns within ponds 5 km from the Bay. Similarly, at mean values for 

all other variables, nest success on individual islands 100 m from San Francisco Bay was predicted to be 

46 percent for avocets, 44 percent for stilts, and 47 percent for terns. Nest success dropped to 36 percent

for avocets, 34 percent for stilts, and 37 percent for terns on individual islands 3 km from the Bay, 

before increasing to 39 percent for avocets, 37 percent for stilts, and 40 percent for terns on islands 5 km 

from the Bay. Coupled with our nest abundance results, these nest success results are notable as they 

may represent adaptive significance, whereby birds select nesting islands that provide greater 

reproductive success. There are numerous reasons why ponds and islands near the San Francisco Bay 

may provide waterbirds greater nest success. Nesting locations close to San Francisco Bay are farther 

from urban centers and therefore may have lower densities of nest predators and disturbance. Human 

disturbance leading to nest abandonment may be reduced in ponds and islands farther from urban 
14

centers. Alternatively, greater nest success in ponds and islands close to San Francisco Bay may be the 

result of the greater nest abundance we observed in ponds and islands close to San Francisco Bay. If 

large waterbird colonies are more effective at driving away potential nest predators than small waterbird 

colonies, the effect of proximity to San Francisco Bay on nest success may instead have more to do with 

nest abundance and communal colony defense than other ecological factors. Nevertheless, the results of 

our nest abundance and nest success analyses at both the pond and island scale indicate that creating 

island nesting habitat close to San Francisco Bay would be most effective at maintaining, and even 

increasing waterbird nesting populations. 

Lastly, waterbird nest success increased slightly with distance away from the surrounding pond 

levee, and was greatest on islands 100–200 m from the surrounding pond levee (fig. 6b). However, 

unlike with our island-scale nest abundance analysis, nest success decreased slightly at island distances 

more than 150 m. Coupled with the results of our nest abundance analyses, these results indicate that 

creating islands between 100 m and 200 m from pond levees may be most beneficial in increasing 

waterbird nest abundance and nest success. 

In conclusion, from our analyses of historical waterbird nesting data, we have identified several 

attributes of ponds, and islands therein, that most benefit nesting waterbirds in South San Francisco 

Bay. Based on these identified attributes, we suggest the following practices for pond and island 

construction and management would likely improve waterbird nesting habitat: 

1. Locate ponds and islands for nesting waterbirds near (<1 km) San Francisco Bay. Such ponds 

and islands exhibited relatively greater nest abundance as well as nest success. Ponds and islands 

located 4–5 km from San Francisco Bay also exhibited relatively high nest abundance, but nest 

success was lower than in ponds near San Francisco Bay.

2. Where possible, construct islands 100–200 m from the nearest surrounding pond levee. 

Although nest abundance increased with increasing island distance from pond levee up to 300 m, 

nest success on islands was greatest on islands 100–200 m from the levee, and decreased at 

distances 200–300 m. 

3. Construct nesting islands more linear in shape, rather than rounded. Although there was no 

difference in nest success between linear and rounded islands, linear islands exhibited as much 

as eight times more nests than rounded islands after controlling for all other island variables 

including size. However, although the recently constructed “linear” islands in Pond SF2 are 

more linear in shape (mean of the residual of perimeter to area regression for all 15 islands = -

33) than the recently constructed round islands in Pond SF2 (mean of the residual of perimeter to 

area regression for all 15 islands = -108), the linear islands in Pond SF2 and Pond A16 are 

actually much more rounded than the islands that support the greatest nest abundance (residual 

of perimeter to area regression = 200). Therefore, it is recommended that islands constructed in 

the future be more linear than those recently constructed in Pond SF2 and Pond A16. Instead, we 

suggest mimicking the linear shape of islands in Pond A2W and the four historical islands at the 

southern end of Pond A16. 

4. Construct three to five nesting islands within multiple ponds, as opposed to constructing many 

islands in just a few ponds. There was no correlation between the number of islands within a 

pond and either nest abundance or nest success. Given this result, constructing fewer nesting 

islands (3–5 islands) within each of several ponds may be more advantageous to waterbirds than 

constructing many nesting islands in a single pond (such as recently created at Pond SF2 [30 

islands] and Pond A16 [20 islands]). In certain situations, predators can effectively cancel 

reproduction across islands within an entire pond, so dispersing nesting islands across several 
15

ponds may decrease the potential for massive reproductive failure. As an example, in 2010, 

California gulls nesting in Pond A6 (prior to the levee breach) severely limited tern reproductive 

success across all islands in adjacent Pond A7 (Ackerman and others, 2014). In a situation such 

as this, having 20 nesting islands spread out among several ponds, rather than in a single pond 

impacted by gulls, likely would lead to greater waterbird productivity. 

5. Construct islands that are 0.05–0.10 ha in size. Individual island size had little effect on 

waterbird nest abundance or nest success. Given this result, and the fact that some of the greatest 

nest abundances were observed on relatively small islands (0.01–0.05 ha), construction of large 

and expensive nesting islands is unnecessary for supporting large numbers of nesting avocets, 

stilts, and terns. However, because island erosion reduces island size over time, constructing 

islands a little larger than the ideal size may improve the longevity of nesting islands. Given that 

birds preferred linear islands, we suggest islands that are approximately 50 m long by 10 m wide 

(0.05 ha) to 100 m long by 10 m wide (0.10 ha). Again, the recently constructed “linear” islands 

in Ponds SF2 and A16 might be too “rounded” at their current size (70 m long by 25 m wide). 

Using GPS Technology to Model Waterbird Nest-Site Selection at Island Patch 

and Microhabitat Scales

Methods

Island Topography

We collected topographic data for the entire surface of 24 nesting islands. At each island, we 

collected UTM coordinates (accuracy: 3.75 cm) and elevation above sea level data (accuracy: 3.75 cm) 

using real-time kinematic GPS (Leica Smart Rover GPS1200) at each point along a predefined grid, 

where points were spaced by 1 m. So as to not disrupt nesting birds, these data were collected in August 

and September of 2011 (23 islands) and 2012 (1 island), immediately after nests had hatched and chicks 

had fledged. Using these data, we developed Digital Elevation Models (DEMs) for each island using 

Inverse Distance Weighted (IDW) interpolation with the Spatial Analyst extension 10.2 for ArcMap 

10.2. From these DEMs, we calculated the maximum slope and corresponding aspect of each 1-m2

island grid cell using the Spatial Analyst extension 10.2 for ArcMap 10.2. We calculated the elevation 

above the water surface for each 1-m2 island grid cell on each island by subtracting the water surface 

elevation of each pond (measured in early May) from the elevation above sea level generated from the 

DEMs. Additionally, we used real-time kinematic GPS to trace each nesting island’s perimeter at the 

water’s edge. UTM coordinates (accuracy: 3.75 cm) and elevation above sea level (accuracy: 3.75 cm) 

data were collected at one second intervals while the perimeter of each nesting island was traversed and 

the water’s edge marked using the GPS unit. These data were then used to create highly precise polyline 

shapefiles that identified the location of the water’s edge for each nesting island. We measured island 

perimeters once in 2011 and potential fluctuations in each pond’s water depth were monitored using 

permanent staff gauges within each pond. During the nesting season, water levels in ponds remained 

relatively static and were similar between 2011 and 2012. In ponds where water levels were different in 

2012 than in 2011, we re-traced island perimeters in 2012. Using these island perimeter polylines, we 

calculated the distance to the water’s edge of the centroid of each island grid cell. 
16

Nest Microhabitat Topography and Vegetation

We entered avocet and tern nesting colonies once a week during the nesting season (April–

August). Newly initiated nests were marked with a uniquely numbered aluminum tag placed outside the 

nest bowl, and a 40-cm tall, colored flag placed 2 m north of the nest. During each weekly visit, we 

recorded clutch size, floated eggs to determine embryo age (Ackerman and Eagles-Smith, 2010), and 

determined whether each nest was active or inactive (abandoned or depredated). We estimated nest 

initiation date by subtracting the clutch size plus the average embryo age of eggs in the nest at the initial 

visit from the date the nest was found. 

For each nest, we measured or derived a suite of microhabitat characteristics at the nest site and 

at one paired site (table 6). The location of the paired site was determined at the time of nest discovery 

and obtained using a randomly generated azimuth (0–359 degrees) and distance (1.0–10.0 m) from the 

nest site. Once the azimuth and distance for a paired site were generated, a two-person team used a field 

compass and tape measure to navigate from the nest to the location of the paired site. Paired sites were 

marked with a uniquely numbered aluminum tag. For each nest, the paired site represented an unused 

available nest site at the time of nest discovery that was close (≤10 m) to the nest. If a randomly 

generated azimuth and distance resulted in a paired site to be in unsuitable habitat (for example, open 

water), we proceeded to the next randomly generated azimuth and distance. 

We placed a 1-m

2 frame centered on the nest bowl, or paired site tag, such that the sides of the 

frame each faced the four cardinal directions. We visually estimated percent cover of vegetation and 

water over the entire 1-m2 area and, using a ruler, measured the average vegetation height (±0.5 cm)

within the 1-m2 area. The 1-m

2

 frame was further divided into nine equal cells (3×3 grid; each cell 

0.33×0.33 m) and the dominant cover type and the maximum vegetation height (±0.5 cm) within each 

cell was recorded. After nesting had concluded, we returned to colony sites and again centered the 1-m2

frame on each nest and each paired site, with the four sides of the frame again facing the four cardinal 

directions. We recorded the UTM coordinates (accuracy: 3.75 cm), and elevation above sea level 

(accuracy: 3.75 cm) of the nest bowl or paired site tag using a real-time kinematic GPS. Additionally, to 

produce a surface topographic profile around each nest and paired site, we measured elevation at 16 

points within the 1-m2 frame; points were spaced every 0.33 m and corresponded to the intersections of 

the 3×3 grid. Lastly, we measured elevation at the two highest and two lowest points anywhere within 

the 1-m2 frame, with the restriction that only one high and one low point could be in any one of the nine 

grid cells. Thus, for each nest and paired site we collected 21 data points (one at the nest or paired site 

tag, 16 at the intersections of the 3×3 grid, and two representing elevation maximums and two 

representing elevation minimums).

Using 20 of these points (excluding the elevation at the nest or paired site tag), we developed 

DEMs at a resolution of 0.33-m2

 around each nest and paired site using IDW interpolation (Spatial 

Analyst extension 10.2 for ArcMap 10.2). Because avocets and terns sometimes build up their nests 

with vegetation and other materials, we decided to omit the elevation taken from the nest bowl when 

interpolating elevation, as this point would create a high point in the center of the surface topography 

that did not exist at the time of nest-site selection. For consistency, we also omitted the elevation taken 

at the paired site tag. 
17

From each nest and paired site-specific DEM, we calculated the maximum slope and 

corresponding aspect (Spatial Analyst extension 10.2 for ArcMap 10.2). We quantified the ruggedness 

of the topography of each nest and paired site by calculating a vector ruggedness measure (VRM) 

following Sappington and others (2007), and using the Vector Ruggedness Measure script for ArcGIS 

(ESRI ArcScripts, http://arcscripts.esri.com). Briefly, VRM is a measure of terrain ruggedness that 

incorporates heterogeneity of slope and aspect. The primary advantage of VRM is that it allows 

ruggedness to be calculated more independently of slope than other methods such that steep, rugged 

areas can be distinguished from areas that are steep but not rugged. The resulting VRM is a unitless 

value ranging from 0 (flat surface) to 1 (highly rugged surface; see Sappington and others [2007] for 

more details). We calculated the elevation above the pond’s water surface for each nest and paired site 

by subtracting the water-surface elevation of each pond from the elevation of the nest and paired site 

above sea level. Lastly, we calculated the distance to water for each nest and paired site using the island 

perimeter polylines previously described. 

Statistical Analyses

Nest-Site Selection at the Island Grid Scale

We investigated characteristics affecting nest-site selection by avocets and terns at two spatial 

scales. First, using the presence or absence of nests among all the 1-m

2 island grid cells on all islands, 

we developed Resource Selection Probability Functions (RSPFs; Manly and others, 2002) to model the 

probability that an avocet or tern would nest in an island grid according to island grid topographic 

features. Using the UTM coordinates from the nest bowls, we assigned each 1-m2 island grid cell as 

used or not used by nesting avocets and used or not used by nesting terns, in each of the 2011 and 2012 

breeding seasons. 

We used generalized linear mixed models (PROC GLIMMIX, SAS/STAT® software, release 

9.4, SAS Institute, Cary, North Carolina), with a binomial distribution and logit link function to model 

the probability that an island grid cell was used for nesting by avocets or terns. We evaluated five fixed 

effects of interest that described each of the island grid cells: (1) Year, (2) mean elevation above the 

water surface (Elevation), (3) Slope, (4) Aspect (analyzed using the cosine of aspect in radians), and (5) 

distance to the water’s edge from the grid cell’s centroid (DistWater). Additionally, because terns and 

avocets are colonial and likely influenced by the presence of nearby nesting birds, we included the 

following additional fixed effects to account for the influence, both in presence and abundance, of 

nearby nesting birds: (6) number of bordering grid cells (0–8) that were used for nesting by conspecifics 

(NumberBorderingConspecificCells), (7) number of bordering grid cells (0–8) that were used for 

nesting by avocets or terns (NumberBorderingOccupiedCells), (8) number of conspecific nests initiated 

in bordering grid cells (NumberBorderingConspecificNests), and (9) number of avocet and tern nests 

initiated in bordering grid cells (NumberBorderingNests). Because these final four variables were highly 

correlated (r

2

 = 0.51–0.97), we allowed only one of these variables (numbers 6–9) to be included in any 

given model. 

We built a relatively balanced set of candidate models based on all combinations of the class 

variable year, and linear and quadratic terms of the fixed effects 2–5, plus the null model. We also 

included nesting island as a random effect in every model. There were 161 combinations of the five 

fixed effects of interest, plus a null model (162 models), which also were evaluated with quadratic terms 

of each one of the four fixed effects that accounted for coloniality, resulting in 810 total candidate 

models. Because avocets and terns did not always nest on the same islands, we conducted separate 

analyses for each species.
18

Nest-Site Selection at the Microhabitat Scale

At the smaller microhabitat scale, we used conditional logistic regression for matched-pairs data 

(PROC LOGISTIC, SAS/STAT software) to model vegetation and micro-topographic characteristics 

affecting nest-site selection by avocets and terns. Individual nests and their corresponding paired site 

were matched with the strata option. We used a binomial distribution and logit link function to model 

the probability that a site was a nest rather than a paired site. We evaluated a total of 11 microhabitat 

variables: (1) vegetation presence (yes or no; VegYN), (2) percent vegetation cover, (%Veg), (3) 

percent water cover (%Water), (4) average vegetation height (VegHt), (5) Slope, (6) Aspect (analyzed 

using the cosine of aspect in radians), (7) surface ruggedness (Ruggedness), (8) distance to water 

(DistWater), (9) elevation above the water surface (Elevation), (10) distance to the nearest active 

conspecific nest (NearestConspecificNest), and (11) distance to the nearest active avocet or tern nest 

(NearestNest). Using nest initiation dates and the dates when nests first became inactive (failed or 

hatched), we developed nest chronologies for each nest and then determined the distance of each nest, at 

the time of its initiation, to the closest active nest. Because of strong correlations among cover variables 

VegYN, %Veg, %Water, and VegHt, we allowed only one of these cover variables to be included in 

any given candidate model. Similarly, because of strong correlation between variables 

NearestConspecificNest and Nearest Nest, we allowed only one of these two variables to be included in 

any given candidate model. Allowing for this restriction, we evaluated a relatively balanced set of

additive models that incorporated all linear combinations of the 11 predictor variables (479 total 

models). 

We next investigated potential differences in the distribution of vegetation around nest sites 

relative to paired sites. We recorded whether or not vegetation was the dominant cover type (as opposed 

to bare ground or water) within each cell of the 3×3 microhabitat grid (each cell 0.33 m×0.33 m) at each 

nest site and paired site. Vegetation was considered the dominant cover type if it covered the most area 

within a cell relative to other cover types. Separately for each of the nine microhabitat grid cells, we 

constructed a 2×2 contingency table with the number of nest sites and the number of paired sites where 

the cell was dominated by vegetation, and the number of nest sites and the number of paired sites where 

the cell was not dominated by vegetation (that is, dominated by bare ground or water). We used 

McNemar’s test (Zar, 1999) for matched pairs with a χ2 statistic to test for differences in vegetation 

cover between nest sites and paired sites in each grid cell. Differences were considered significant at p

less than or equal to 0.05. 

Model Selection

For both the island grid and microhabitat scales of analysis, we ranked models using an 

information-theoretic approach and AICc(Burnham and Anderson, 2002). The model with the lowest 

AICc score was considered to be the most parsimonious, and we used ΔAICc between the best model 

and each other model in the candidate set to assign model rank. We considered models with a ΔAICc

score less than or equal to 2.0 to be competitive and calculated the beta parameters of the variables by 

model-averaging all models in the candidate set. The weight of evidence for each model was determined 

using wi, defined as the relative likelihood of a model given all of the models in the candidate set. We 

used evidence ratios to compare the relative weight of support between models. At the microhabitat 

scale, we further evaluated the effect size for each variable using standardized odds ratios calculated 

from the model-averaged parameter estimates. Odds ratios for continuous variables were standardized 

by calculating them at the first and third quartile values of the paired site data (Harrell, 2001). In this 

way, odds ratios for each variable were scaled relative to the amount of variability of that variable 

across the study area, thereby allowing for direct comparison of effect size among variables.
19

Results and Discussion

Nest-Site Selection at the Island Grid Scale

We recorded precise locational data for 1,367 avocet and tern nests on the 24 islands for which 

we collected island topographic data, including 345 avocet nests (2011: n = 263, 2012: n = 82) and 

1,022 tern nests (2011: n = 506, 2012: n = 516). Avocets initiated nests between March 24 and July 19 

(mean ±1 standard deviation: May 19 ±27 days, n = 340), whereas terns initiated nests between April 25 

and July 22 (mean ±1 standard deviation: June 17 ±14 days, n = 1,001). However, whereas avocets 

nested in all ponds where terns nested, terns did not nest in all avocet nesting ponds. Considering only 

ponds where both avocets and terns nested, avocets initiated nests between April 4 and July 19 (mean 

±1 standard deviation: May 29 ±22 days, n = 192). Avocets nested on 23 islands in 8 ponds in 2011 and 

9 islands in 5 ponds in 2012 (a total of 24 different islands), and terns nested on 10 islands in 5 ponds in 

2011 and 8 islands in 4 ponds in 2012 (a total of 11 different islands; table 7). Differences in number of 

islands and number of nests between years were due to differences in nesting densities and locations of 

birds, and not by our level of survey effort. Only years in which an island had one or more nests and 

where we were able to map all nests on an island were included in the analysis.

Among avocets, the most parsimonious model describing the probability that an 1-m2

 island grid 

cell was used for nesting included year, a linear term for slope, and quadratic terms for elevation, 

distance to water, and the number of conspecific nests initiated in bordering cells; had an Akaike weight 

of 0.30; and was 2.14 times more likely than the next best model (table 8a). Three other models were 

competitive (ΔAICc < 2.0), and each model was similar to the best model but also included either a 

quadratic term for slope, the number of bordering cells with nesting conspecifics rather than the number 

of conspecific nests in bordering cells, or aspect. In fact, all models that contained the variables in the 

best model had a cumulative weight of 0.70. To further evaluate the importance of the variables in the 

best model, we compared evidence ratios between the best model and an identical model but with one of 

the variables removed. Using this approach, we estimated that the best model was 2.29×108 times more 

likely than the best model without elevation, 9.48×107 times more likely than the best model without 

year, 1.70×105 times more likely than the best model without distance to water, 3,500 times more likely 

than the best model without the number of conspecific nests initiated in bordering cells, and 27 times 

more likely than the best model without slope. Island grid cells used by nesting avocets averaged (± 

standard deviation) 0.62±0.28 m above the water surface, 2.69±2.62 m from the water’s edge, had a 

slope of 4.8±3.0 degrees, were bordered by 0.4±0.7 cells that were used by nesting avocets and 1.5±2.0 

cells used by nesting avocets and terns, and had 0.4±0.7 avocet nests and 2.2±3.4 avocet and tern nests 

initiated in all bordering cells over the nesting season. 

Model-averaged parameter estimates showed the probability that avocets nested in an island grid 

cell increased with the grid cell’s elevation up to a peak approximately 0.8 m above the water surface, 

beyond which the probability of nesting in a grid decreased (fig. 7a). Similarly, the probability that 

avocets nested in an island grid cell increased with the grid cell’s distance to the water’s edge up to a 

peak of approximately 7 m and then decreased thereafter (fig. 7b). Moreover, the probability that 

avocets nested in a grid cell increased with slope, such that avocets preferred to nest on more steep 

terrain, up to a peak of approximately 15 degrees and then decreased slightly thereafter (fig. 7c).

Conversely, there was little effect of the aspect of the island grid cell on avocet nesting probability (fig. 

7d). Finally, the probability that avocets nested in an island grid cell decreased with both the number of 

bordering cells used by nesting avocets (fig. 7e) and the total number of avocet nests initiated in 

bordering cells over the entire nesting season (fig. 7f). In contrast, the probability that avocets nested in 

an island grid cell increased with both the number of bordering cells used for nesting by avocets and 
20

terns, and the total number of avocet and tern nests initiated in bordering cells over the entire nesting 

season, up to a peak of approximately 5 bordering cells and 11 bordering nests, and decreased thereafter 

(figs. 7e and 7f).

Among terns, the most parsimonious model describing the probability that an island grid cell 

was used for nesting included quadratic terms for elevation, distance to water, and the number of 

bordering grid cells used by nesting terns; had an Akaike weight of 0.09; and was 1.04 times more 

likely than the next best model which was similar to the best model but included a linear rather than a 

quadratic term for elevation (table 8b). Nine other models were competitive (ΔAICc < 2.0), and each 

was similar to the best model but included the variables year, aspect, slope, and (or) had a linear term 

for elevation instead of a quadratic term. In fact, models that contained elevation, distance to water, and 

the number of bordering grid cells used by nesting terns had a cumulative weight of 1.00. Using 

evidence ratios, the best model was 2.67×1056 times more likely than the best model without number of 

bordering grid cells used by nesting terns, 8.85×1012 times more likely than the best model without 

distance to water, and 2.14×107 times more likely than the best model without elevation. Island grid 

cells used by nesting terns averaged (± standard deviation) 0.61±0.25 m above the water surface, 

0.93±0.73 m from the water’s edge, had a slope of 5.5±3.4 degrees, were bordered by 3.6±1.9 cells that 

were used by nesting terns and 3.7±1.9 cells used by nesting avocets and terns, and had 5.2±3.5 tern 

nests and 5.7±3.8 avocet and tern nests initiated in all bordering cells. 

Model-averaged parameter estimates showed that the probability of a tern nesting in an island 

grid cell increased with elevation (fig. 8a). The probability of a tern nesting increased with distance to 

the water’s edge up to a peak approximately 2.0 m from the water’s edge, and then decreased at greater 

distances (fig. 8b). In contrast to avocets, terns were more likely to nest in island grid cells with flat to 

shallow slopes (fig. 8c), and were slightly more likely to nest in south-facing than in north-facing grid 

cells (fig. 8d). The probability that an island grid cell was used by nesting terns increased as the number 

of bordering cells used by terns, and bordering cells used by avocets and terns over the entire nesting 

season, increased (fig. 8e). The probability that an island grid cell was used by nesting terns also 

increased as the total number of tern nests, and the total number of avocet and tern nests, initiated in 

bordering cells over the entire nesting season increased up to a peak of approximately 10 tern nests and 

12 avocet and tern nests, and decreased thereafter (fig. 8f). Interestingly, year played no role in the 

probability of a tern nesting in an island grid cell, unlike in avocets where year was a major factor 

influencing nesting probability. 

Nest-Site Selection at the Microhabitat Scale

We measured or derived all 11 microhabitat variables at both the nest site and a paired site for 

662 nests, including 229 avocet nests (2011: n = 161, 2012: n = 68) and 433 tern nests (2011: n = 202, 

2012: n = 231; table 7).

The most parsimonious model describing avocet nest microhabitat included the variables 

vegetation presence, distance to nearest active conspecific nest, and aspect; had an Akaike weight of 

0.16; and was 2.26 times more likely than the next best model (table 9a). Two other models were 

competitive (ΔAICc < 2.0), and each was the same as the best model but also included either surface 

ruggedness or distance to water. In fact, models that contained vegetation presence, distance to nearest 

active conspecific nest, and aspect had a cumulative weight of 0.86. The best model was 6.09×1011

times more likely than the best model without vegetation presence, 408 times more likely than the best 

model without distance to nearest active conspecific nest, and 2.9 times more likely than the best model 

without aspect. 
21

Model-averaged parameter estimates showed that the probability that a microhabitat was used 

for nesting by avocets decreased as the microhabitat distance from an active conspecific nest or active 

avocet or tern nest increased, increased as the amount of vegetation around the nest increased, increased 

as the average vegetation height around the nest increased, decreased as aspect changed from a 

southward to a northward orientation, and was greater when vegetation was present (table 10).

Standardized odds ratios (obtained using the values of the first and third quartiles of the paired site data) 

indicated that distance to nearest conspecific nest had the greatest effect size among variables, followed 

by distance to nearest nest of either species, vegetation presence or absence, percent vegetation cover, 

aspect, and average vegetation height. A microhabitat located 3.7 m (first quartile distance for paired 

sites) from the nearest active avocet nest was 5.9 times more likely to be used for nesting by an avocet 

than a microhabitat 20.6 m (third quartile distance for paired sites) from the nearest active avocet nest. 

Additionally, avocet nest sites were 4.1 times more likely to have vegetation present than paired sites, 

with 65 percent of avocet nest sites having some vegetation compared to only 43 percent of paired sites.

Lastly, a microhabitat with a more southern aspect (138 degrees, first quartile aspect for paired sites) 

was 1.6 times more likely to be used by nesting avocets than a microhabitat with a more northern aspect 

(53 degrees, third quartile aspect for paired sites). 

The most parsimonious model describing tern nest microhabitat included the variables, percent 

vegetation cover, elevation, distance to nearest active conspecific nest, and aspect; had an Akaike 

weight of 0.20; and was 1.69 times more likely than the next best model (table 9b). Three other models 

were competitive (ΔAICc < 2.0), and each was the same as the best model but also included either slope, 

surface ruggedness, or both. Models that contained percent vegetation cover, elevation, distance to 

nearest active conspecific nest, and aspect had a cumulative weight of 0.66. The best model was 860 

times more likely than the best model without percent vegetation cover, 240 times more likely than the 

best model without elevation, 23 times more likely than the best model without distance to nearest 

active conspecific nest, and 6 times more likely than the best model without aspect. 

Model-averaged parameter estimates showed that the probability that a microhabitat was used 

for nesting by terns increased with elevation, decreased as a the microhabitat distance from an active 

tern nest increased, increased as the percent cover of vegetation around the nest increased, decreased as 

aspect changed from a southward to a northward orientation, and was greater when vegetation was 

present (table 10). Standardized odds ratios indicated that percent vegetation cover had the greatest 

effect size among variables, followed by elevation, vegetation presence, aspect, and distance to nearest 

conspecific nest. A microhabitat with 75 percent vegetation cover (third quartile vegetation cover for 

paired sites) was 2.6 times more likely to be used for nesting by terns than a microhabitat with 0 percent

vegetation cover (first quartile vegetation cover for paired sites). Similarly, microhabitats at an elevation 

of 0.72 m (third quartile elevation for paired sites) were 2.5 times more likely to be used for nesting by 

terns than a microhabitat at an elevation of 0.37 m (first quartile elevation for paired sites). Tern nest 

sites were 1.6 times more likely to have vegetation present than paired sites, with 76 percent of tern nest 

sites having some vegetation compared to only 67 percent of paired sites. Microhabitats with a more 

southern aspect (141 degrees, first quartile aspect for paired sites) were 1.4 times more likely to be used 

by nesting terns than a microhabitat with a more northern aspect (48 degrees, third quartile distance for 

paired sites). Finally, a microhabitat located 0.6 m (first quartile distance for paired sites) from the 

nearest active tern nest was 1.3 times more likely to be used for nesting by an avocet than a microhabitat 

2.1 m (third quartile distance for paired sites) from the nearest active tern nest. 
22

We compared dominant cover type between nest sites and paired random sites in each of the 

nine (3×3 grid; each cell 0.33×0.33 m) microhabitat grid cells for 316 American avocet and 648 

Forster’s tern nests. Pickleweed and alkali-heath were the most frequently occurring plant species on 

nesting islands. Other less common species included slenderleaf iceplant (Mesembryanthemum 

nodiflorum), brass buttons (Cotula coronopifolia), ripgut brome (Bromus diandrus), and saltgrass 

(Distichlis spicata). Vegetation was the dominant cover type in the center microhabitat grid cell (the cell 

where the nest bowl was located) in 30 percent of avocet nests compared to only 21 percent of paired 

random sites (χ2 = 27.16, p < 0.0001, fig. 9a). Moreover, significantly more avocet nest sites were 

dominated by vegetation in cells north (χ2 = 9.99, p = 0.002) and east (χ2 = 4.50, p = 0.03) of the center 

cell that contained the nest bowl (fig. 9a). Among terns, a significantly greater number of nest sites were 

dominated by vegetation within each of the nine microhabitat grid cells relative to paired sites (all χ2 ≥ 

15.20, p < 0.0001), with differences in the proportion of nest sites dominated by vegetation relative to 

paired sites ranging from 6 to 11 percent among the nine microhabitat grid cells (fig. 9b). For example, 

vegetation was the dominant cover type in the center microhabitat grid cell (the cell where the nest bowl 

was located) in 57 percent of tern nests compared to only 47 percent of paired random sites. 

Furthermore, much like with avocet nests, the proportion of tern nest microhabitat grid cells dominated 

by vegetation was greatest in cells north, northeast, and east of the center microhabitat cell containing 

the nest bowl. 

Summary

Island topography, vegetation cover, and social interactions all greatly affected nest-site 

selection by avocets and terns in South San Francisco Bay. Both species avoided nesting at low 

elevations and near the water’s edge. For waterbirds nesting in tidal areas, nests at higher elevations 

often exhibit greater nest success, because of a lower likelihood of nest flooding (Rounds and others, 

2004; Owen and Pierce, 2013). As such, many waterbirds nesting in tidal areas often avoid low 

elevation, near-water nest sites that are more susceptible to flooding (Burger and Shisler, 1980; Howe 

and others, 1982; Storey and others, 1988; Lauro and Burger, 1989). Although the islands in this study 

were within former salt ponds, and not exposed to daily changing tides, wave action from high winds 

often inundated low-lying island areas, particular those on the windward side of the island, and pond 

levels can fluctuate at extreme high and low tides depending on the location and size of the water 

control structures on the ponds. Additionally, fluctuations in managed water levels also flooded nests. 

Indeed, in some of the managed ponds, flooding accounted for more than 60 percent of avocet nest 

failures (Ackerman and others, 2013). Thus, avoidance of low-lying, near-water island areas by nesting 

avocets and terns likely improves the probability of nest survival in South San Francisco Bay. 

The probability of avocets and terns nesting within a 1-m2 island grid cells also varied according 

to slope. However, whereas avocets preferred steeper slopes (peak probability of nesting at 15 degrees), 

terns preferred flat to shallow slopes of less than 10 degrees (peak probability of nesting at 6 degrees).

Previous studies have shown slope to be an important determinant of nest-site selection in terns and 

shorebirds (Burger, 1988; Burger and Gochfeld, 1990; Whittingham and others, 2002; Anteau and 

others, 2012), with birds typically selecting flat nest sites. For terns nesting in South San Francisco Bay, 

nesting in flat island areas may prevent eggs from rolling away when nests are unattended, particularly 

during high winds. Aspect of island grid cells had little to no effect on nesting probability of avocets and 

terns, as avocets nested at relatively equal probabilities regardless of the orientation of island grid cells, 

and terns showed only a modest decrease in nesting probability as island grid cell’s orientation became 

more northern-facing. Conversely, nest microhabitat selection was effected by aspect as both avocets 

and tern nest sites were more likely to have south-facing slopes than north-facing slopes relative to 
23

paired sites, indicating that at the smaller microhabitat scale both species preferred to orient their nests 

southward. A south-facing nest orientation, coupled with at least a moderate slope, may afford some 

protection to nests from the often strong northwestern winds that are common to South San Francisco 

Bay. Lastly, surface ruggedness at the microhabitat scale did not vary between nest sites and paired 

random sites, indicating that microhabitats at avocet and tern nests were no more smooth or rugged than 

what was available on the island. 

The probability of avocet nesting varied between the two years of the study, such that peak 

nesting probabilities were almost 50 percent lower in 2012 relative to 2011 (fig. 7). Conversely, tern 

nesting probabilities were consistent between years (fig. 8). Indeed, whereas similar numbers of tern 

nests were initiated among the 24 study islands between years (506 in 2011and 516 in 2012; table 7), 

only 82 avocet nests were initiated among the 24 islands for which we had island topographic data in 

2012 compared to 263 in 2011. Lowered water levels in Pond A17 in 2012, relative to 2011 caused the 

formation of the Northeast Island in Pond A17, and 45 avocet nests were found on this island in 2012, 

22 of which were used in our microhabitat scale analysis. Even after accounting for the 27 avocet nests 

found in 2011 on islands not monitored in 2012 (Islands 1 and 2 on Pond E2; Northwest Island on Pond 

AB1), avocet nesting effort on islands for this study was much lower in 2012. In fact, with the exception 

of Surprise Island 8 on Pond AB2, avocet nesting effort was lower on every island monitored in 2012 

compared to 2011 (table 7). 

At two ponds, management activities likely played a role in reduced avocet numbers in 2012. In 

2012, Pond A16 was drained in preparation for the construction of new nesting islands (completed prior 

to the 2013 breeding season) associated with the South Bay Salt Pond Restoration Project. Lack of 

suitable nearby foraging habitat and predator access to nesting islands likely made Pond A16 

unattractive to nesting avocets and terns in 2012. Thirty islands at Pond SF2 (eight of which were 

included in this study) were constructed prior to the 2011 breeding season, also as part of the South Bay 

Salt Pond Restoration Project. After a positive response the first year after island construction (154 nests 

on 30 islands in 2011), only four avocet nests were found on Pond SF2 islands in 2012. Extensive 

cracking of island surfaces was evident on Pond SF2 islands in 2011 and 2012, and shorebird chicks 

that had fallen into these cracks were discovered on a few occasions. If such chick losses were 

widespread, and if reproductive success was very low in general, avocets may have rejected these 

islands as nesting habitat in 2012. Among all islands, even islands where the number of tern nests 

increased or were similar between years, avocet nest numbers were much lower in 2012. Unlike terns, 

avocets seemed not to move to new island nesting areas as their former nesting habitat changed (such as 

the loss of Pond A8 and Pond A16, which once hosted as many as 400 avocets annually), and thus 

population size of avocets has declined substantially in the South San Francisco Bay (J.T. Ackerman, 

unpub. data). 

Microhabitats of avocet and tern nests varied in the distribution and extent of vegetation cover. 

Relative to paired sites, tern nest sites were more likely to have vegetation present in each of the nine 

(0.33×0.33 m) microhabitat grid cells (fig. 9). Thus, although terns clearly preferred nest sites 

surrounded by vegetation, there appeared to be little, if any, preference for vegetation structure at 

particular locations around the nest. Conversely, avocet nests were only more likely to have vegetation 

present in the center microhabitat grid cell (the cell where the nest bowl was located) and in cells 

immediately north and east of the nest bowl (fig. 9a). Furthermore, the greatest difference in the 

likelihood of containing vegetation between avocet nest and paired sites occurred in the center cell that 

contained the nest bowl. Some shorebirds, including avocets, often place their nest near conspicuous 

objects such as livestock dung piles, rocks, and dirt mounds (Grover and Knopf, 1982; Colwell and 

Oring, 1990), a behavior that may make the incubating bird less conspicuous to predators, particularly in 
24

areas with little vegetation cover (Allen, 1980). Selection for vegetation in the center microhabitat grid 

cell may reflect avocets’ preference for nesting near conspicuous objects, in a relatively sparsely 

vegetated environment. 

For avocets and terns, the probability of nesting in an island grid cell increased with the number 

of bordering grid cells that were used for nesting by terns and with the number of tern nests initiated in 

the bordering grid cells. Conspecific attraction to nesting sites has been documented in many colonial 

waterbird species, and has been used to great effect in restoration of seabird breeding colonies (Jones 

and Kress, 2012), including Forster’s terns (Ward and others, 2011). The presence of many conspecifics 

may signal to individual terns the quality of the breeding patch. Moreover, like many terns, Forster’s 

terns display aggressive predator mobbing behaviors (McNicholl and others, 2001). In San Francisco 

Bay, California gulls are important predators of Forster’s tern chicks, accounting for as much as 54 

percent of chick deaths (Ackerman and others, 2014). By nesting among many other terns, individual 

terns, as well as avocets, may reduce the probability that their eggs or chicks are depredated through 

greater communal mobbing of predators, as well as predator satiation (Götmark and Andersson, 1984). 

In conclusion, based on our analyses of nest-site selection at both the island grid and 

microhabitat scales, we suggest the following practices for pond and island construction and 

management would likely improve waterbird nesting habitat: 

1. Construct nesting islands with abundant area 0.5–1.5 m above the water surface. Avocet 

preferred habitat was 0.5–1.0 m above the water surface, whereas terns’ preference increased 

with elevation up to 1.3 m. Thus, islands with much of their area at elevations below 0.5 m will 

be limited in their ability to support large numbers of nesting waterbirds. This preference 

combined with the likelihood for erosion over time, indicates that islands be constructed with 

ample area that is 0.5–1.5 m above the water surface.

2. Construct nesting islands with abundant area within 10 m from the water’s edge. The probability 

of nesting peaked at approximately 7 m from the water’s edge for avocet nests, and 2 m from the 

water’s edge for tern nests. Given these results, large, rounded islands which have considerable 

area more than 10 m from the water’s edge are less beneficial to nesting avocets and terns. Like 

our results at the largest spatial scale, this result indicates that linear islands are more conducive 

to bird nesting than are rounded islands because, for a given island size, a linear shape allows for 

more area within 10 m of the water’s edge.

3. Construct nesting islands with a mosaic of slopes ranging from flat to moderately steep (21 

degrees). Avocets were more likely to nest in steep island grid cells whereas terns preferred flat 

grid cells. Thus, high topographic relief on islands is suggested, which is often at odds with the 

final smoothing of islands that is typically done during island construction. 

4. Construct nesting islands with abundant area with south-facing slopes. Although avocets showed 

no preference for aspect of island grid cells, terns preferred to nest on south-facing slopes. To 

maximize the amount of area on linear islands with south-facing slopes, islands may be oriented 

west-to-east from end-to-end. 

5. Ensure nesting islands contain patches of short vegetation, ranging from 10 to 100 percent cover, 

as well as area with little (<10 percent cover) or no cover, but tall vegetation is not ideal. Both 

avocets and terns were more likely to nest in microhabitats with vegetation (65 percent of avocet 

nests, 76 percent of tern nests) than without it. Yet, many avocet nests were in sparsely vegetated 

areas, suggesting that islands with complete vegetation cover would not be conducive to nesting 

by avocets.
25

Factors Influencing Wintering Waterbird Abundance on Created Islands in 

Pond SF2

Methods

Island-Scale Surveys

To evaluate the importance of island features to waterbirds wintering in the SBSP Restoration 

Project area, we conducted studies at several spatial scales. We focused on one managed pond (Pond 

SF2) in which 30 islands were created for nesting and roosting birds. We used instantaneous scan 

sampling (Altmann, 1973) of all birds present on the Ravenswood Pond SF2 weekly from October 1 

through May 12 during 2010–2011 and 2011–2012, with 10×40 binoculars and a 60× spotting scope. 

We used an aerial photograph of the pond that was superimposed by 50×50 m UTM grids to spatially 

record birds in specific locations. We conducted a diurnal high tide and low tide count within one 24-

hour period for each weekly survey. High tide counts were conducted within 1.5 hours of the diurnal 

high tide (ranging from 5.1 to 10.3 ft at the nearest NOAA tide gauge station) and provided a count of 

the maximum number of birds using the pond while their low tide foraging and roosting sites were 

flooded. Low tide counts were conducted within 1.5 hours of the diurnal low tide (ranging from -1.8 to 

3.6 ft) and provided the minimum number of birds using the pond (Dias and others, 2006). Counts were 

conducted in all environmental conditions except for high winds (>40 kilometers per hour) or heavy 

rain. Observers underwent extensive training and did not conduct surveys until their counts consistently 

matched those of a senior observer. 

Elevated viewing platforms provided a comprehensive view of the whole study area. We 

assigned birds to a 50×50 m grid cell and to one of six habitat types: man-made structure, open water, 

island, shallow water surrounding the island, levee, or exposed pond bottom. All birds were identified to 

species with the exception of long-billed dowitchers (Limnodromus scolopaceus) and short-billed 

dowitchers (L. griseus) and greater scaup (Aythya marila) and lesser scaup (A. affinis), because they 

were difficult to distinguish in the field. 

Island-Scale Environmental Data

We conducted an elevation survey of all 30 islands using a Leica VIVA real time kinematic 

GPS) rover unit capable of collecting survey-grade elevation and x and y position data (UTM) from the 

Leica Smartnet system (accuracy: ±3 cm x, y, and z; Leica Geosystems Inc., Norcross, Georgia). The 

unit averaged ±2.5 cm vertical error at a reference benchmark (X 552 1956 Mare Island), which is 

within the stated error of the unit. All data were collected and reported in meters with horizontal datum 

UTM North American Datum of 1983 (NAD83) zone 10 and vertical datum North American Vertical 

Datum of 1988. 

For each island we created a DEM using Spatial Analyst (ArcGIS® 9.3.1, ESRI, Redlands, 

California) at 1-m resolution. We used the Inverse-Distance Weighting method to interpolate the 

elevation point data within the boundary of each island outline. The island digital elevation models were 

used with Spatial Analyst tools to calculate the mean slope of each island. This tool determines the slope 

of each 1-m grid of the elevation model. We averaged all 1-m grid slopes to obtain a mean slope per 

island. 
26

The centerlines of pond levees were digitized using 2005 and 2009 National Agricultural 

Imagery Program (NAIP) imagery (1-m resolution, UTM NAD83 zone 10). The islands, mudflat edge, 

highway edge, and power line were digitized from a 2010 aerial image with 11-cm resolution. We

calculated distance from the center point of each island to the mudflat edge.

Island-Scale Analysis

To examine temporal trends, we classified surveys into three seasons: autumn (October 1–

December 21), winter (December 22–March 19), and spring (March 20–May 30) for each year. Habitats 

were grouped into three categories: levees, ponds (man-made structures, open water, exposed pond 

bottom) and islands (island and shallow water adjacent to the islands). Because we focused on nonbreeding birds, any nesting species observed on the pond were noted but not included in analyses. 

Species were assigned to foraging guilds for analysis, which included: 

• dabbling ducks—for example, northern shovelers (Anas clypeata) and American wigeons (A. 

americana); 

• diving ducks— for example, ruddy ducks (Oxyura jamaicensis); 

• geese—for example, Canada goose (Branta canadensis); 

• eared grebes (Podiceps nigricollis); 

• piscivores—for example, double-crested cormorants (Phalacrocorax auritis), and American 

white pelicans (Pelecanus erythrorhynchos); 

• terns—for example, Forster’s terns; 

• gulls—for example, ring-billed gulls (Larus delawarensis); 

• herons—for example, great egrets (Ardea alba); 

• medium shorebirds—for example, marbled godwits (Limosa fedoa), willets (Tringa 

semipalmata), and dowitchers (Limnodromus spp.); 

• small shorebirds—for example, Western sandpipers and dunlin (C. alpina); and 

• raptorsfor example, peregrine falcon (Falco peregrinus; table 11).

To determine if there were differences in total abundance by island, we used a Kruskal-Wallis 

non-parametric test followed by Tukey’s post hoc tests for island pairwise comparisons. To examine 

whether relative use by guilds on islands varied, we conducted a multivariate analysis of variance 

(MANOVA) using relative abundances as the response variables. We used binary logistic regression to 

examine whether island shape and size had an effect on guild presence or absence. Finally, we used 

linear mixed models to explore how abundances of guilds varied in relation to island characteristics. We 

fit the model with island shape (roundedopposed to linear) as a fixed effect and used mean island slope, 

distance from center of island to edge of mudflat, area, and island perimeter as random effects. All 

statistical analyses were conducted with SPSS®, v.20.0 (IBM Corporation, New York). Data are 

presented as means ±95-percent confidence intervals, unless otherwise noted. 
27

Results and Discussion

Island-Scale Surveys

We conducted 112 complete surveys during the two field seasons, and observed 67 bird species 

(table 11). Western sandpiper was the most abundant species, with 89,097 individuals. Bird abundance 

was lower in the winter than in autumn or spring. At high tide, we observed the highest abundance of 

birds using the islands in spring; whereas at low tide, we observed higher abundances of bird using 

islands during autumn and winter (fig. 10). 

The majority (82 percent) of bird observations were on the pond surface compared with 14 

percent on islands and 4 percent on levees. We observed most birds (81 percent) during high tide counts 

(n = 56), of which 31,016 (15 percent) birds were on islands, 8,924 (4 percent) birds were on levees, and 

171,724 (81 percent) birds were in the water or on the pond bottom. Low tide counts were lower, with

6,094 (12 percent) birds on islands, 1,349 (3 percent) on levees, and 43,826 (88 percent) on pond bottom 

(fig. 11). Overall bird densities varied by habitat type and by tide, but followed the same trend as 

abundance. 

Island bird density was the highest at 0.57 birds/m2

 during high tide and 0.11 birds/m2 during 

low tide. Open water and pond bottom had the next highest density of birds, with 0.33 birds/m2 during 

high tide and 0.08 birds/m2

 during low tide. Levees had the lowest density of birds with 0.19 birds/m2

during high tide and 0.03 birds/m2

 during low tide. 

Island use was widespread but not evenly distributed among all 30 islands at Pond SF2 and was 

affected by both season and tide. Total abundance differed significantly among islands (H(29) = 93.5, p 

< 0.001). There was a similar result when we evaluated high and low tide abundances separately (high 

tide H(29) = 55.7, p = 0.01; low tide H(29) = 68.6, p < 0.001). At high tide, total abundance for Island 24 

was the highest and differed significantly from all other islands except Islands 14, 17, and 25 (fig. 12).

At low tide, no islands had abundances that were statistically different from one another. 

The relative abundance of some guilds varied across islands (table 12). At low tide, medium 

shorebirds, dabbling ducks, and gull and tern abundances were significantly different among islands. At 

high tide, differences among islands were observed with small and medium shorebirds, dabbling ducks, 

piscivores, and gulls and terns. 

At low tide, the presence of dabbling ducks (p = 0.01) was most commonly associated with 

smaller islands, whereas the presence of gulls (p = 0.03) was associated with larger islands (table 13).

At high tide, island shape significantly improved the prediction of greater presence of small shorebirds 

on rounded islands (p = 0.01) and herons (p = 0.02) on linear islands. At low tide, island shape 

significantly improved the prediction of greater presence of piscivores (p = 0.048). The inclusion of 

island size significantly improved the prediction of dabbling ducks on smaller islands (p = 0.03). The 

presence of gulls was better predicted by a model including both size and shape, as gulls more often 

used linear (p = 0.01) and large (p = 0.01) islands. All random effects tested (mean slope of island, 

island perimeter, distance to mudflat or island area) did not improve the models’ predictions of the 

abundance of any guilds. 

Summary

Previous studies have shown that waterbirds, especially shorebirds, use tidal flats for roosting 

and foraging at low tide when the habitat is exposed, and use alternate roosting and foraging habitats at 

high tide (Burger and others, 1977; Warnock and Takekawa, 1995; Long and Ralph, 2001; Dias and 

others, 2006). Similarly, we observed high abundances of waterbirds using Pond SF2 and the islands 

therein at high tide when the adjacent mudflats were inundated. 
28

The lower abundance of birds in the winter and higher abundance of birds in the autumn and 

spring reflect bay-wide migratory patterns (Page and others, 1999; Franks and others, 2014). Migratory 

patterns, along with different foraging preferences, may account for changes in seasonal use among 

islands at high and low tide. Pond SF2 islands were most heavily used during the spring at high tide, 

predominantly by sandpipers. Sandpipers have a diffuse autumn migration (July–October), whereas 

their spring migration has a narrower time window and their abundance is more concentrated (Franks 

and others, 2014). This pattern was not observed at low tide, as sandpipers disperse to tidal mudflats to

forage. In contrast, dabbling ducks dominated the islands at low tide. Although dabbling ducks are not 

typically considered mudflat foragers, they are known to forage in estuary impoundments (Baldwin and 

Lovvorn, 1994). Because of this difference in foraging locations, dabbling ducks can spend the tidal

cycle in the pond, roosting on the islands and foraging within the pond. 

Use of roost sites can be variable and dynamic with few sites serving as primary roosts and 

numerous sites used infrequently (Conklin and others, 2007, 2008). Among Pond SF2 islands, Islands 

24 and 25 were used as roosting sites more than the other islands. For shorebirds, proximity to foraging 

areas influences use of roosts (Furness, 1973; Warnock and Takekawa, 1996; Dias and others, 2006; 

Conklin and others, 2008). The maximum distance (< 1 km) of any island on Pond SF2 to the adjacent 

mudflat is less than the maximum distance small shorebirds are known to travel between roosts and 

foraging habitat (Warnock and Takekawa, 1996; Dias and others, 2006), thus all islands within Pond 

SF2 are likely within range for shorebirds to use them as high tide roosting sites. When small shorebirds 

were present on the pond, regardless of tide, they were more likely to be observed on round islands. The 

round islands might provide better protection from the wind, because high winds can cause birds to 

abandon roost locations (Handel and Gill, Jr., 1992; Burton and others, 1996). Alternatively, these larger 

islands may support a larger flock, which could be useful for anti-predator behavior in smaller species. 

Herons and egrets were present more often on linear islands, perhaps because the smaller, scalloped 

edge areas provide multiple shallow sites for stalking fish. 

Our study at Pond SF2 found that the waterbirds using newly-created islands for roosting were 

most influenced by island size and shape. Overall, the islands had higher densities of birds than either 

the open water or levee areas of Pond SF2, and therefore were considered to increase the number of 

birds using the pond. Island habitats might become even more important as the SBSP Restoration 

Project plans to restore 50–90 percent of the managed ponds to tidal marsh, and the availability of roost 

sites may be reduced in the future. Thus, the islands in Pond SF2 and other remaining managed ponds 

may become even more important as roosting sites.

Overall, from our analysis of the newly constructed islands in Pond SF2, we have identified the 

following island features that benefit wintering and migratory waterbirds:

1. Small shorebird use of islands at high tide suggests that these islands provided important 

roosting habitat when tidal mudflats were inundated. Small shorebirds (sandpipers) were more 

likely to use round islands as opposed to linear islands at high tide during spring. 

2. Preferences for island shape and size differed by guild. During the winter, dabbling ducks were 

more likely to use small islands as opposed to large islands during low tide. Wading birds 

(herons) and piscivores were more likely to use linear islands as opposed to round islands. 

Importantly, gulls were more likely to use large, linear islands versus small, round islands. 
29

Importance of Islands for Wintering Waterbirds Using Managed Ponds

Methods

Pond and Grid-Scale Waterbird Surveys

We conducted monthly waterbird surveys of ponds in the Alviso (n = 25), Eden Landing (n = 

28), and Ravenswood (n = 10) complexes from October 2002 through April 2013. We surveyed during 

high tide when nearby mudflats were inundated and shorebird abundances within ponds were likely at 

their peak (Warnock and others, 2002). Species and abundance were mapped on 250×250 m (6.25-ha) 

UTM grids to document the spatial distribution of birds within ponds. We used existing landmarks to 

identify the spatial locations of birds. We counted all species with binoculars and spotting scopes from 

levee vantage points. We documented whether each bird was foraging or roosting based on behavior at 

the time of observation. Species were assigned to foraging guilds for analysis (table 15), which 

included: 

• dabbling ducks—for example, northern shovelers (Anas clypeata) and American wigeons (A. 

americana); 

• diving ducks—for example, ruddy ducks (Oxyura jamaicensis); 

• piscivores—for example, Forster’s terns, double-crested cormorants (Phalacrocorax auritis), 

and American white pelicans (Pelecanus erythrorhynchos); 

• gulls—for example, ring-billed gulls (Larus delawarensis); 

• herons—for example, great egrets (Ardea alba); 

• medium shorebirds—for example, marbled godwits (Limosa fedoa), willets (Tringa 

semipalmata), and dowitchers (Limnodromus spp.); and

• small shorebirds—for example, western sandpipers and dunlin (C. alpina). 

In addition we identified five species of interest (American avocet, black-necked stilt, eared grebe, 

Forster’s tern, and western sandpiper), to evaluate individually. Individual species were also included in 

their respective guild for analysis. 

Pond and Grid-Scale Environmental Data

We measured water salinity monthly in each pond using a Hydrolab® Minisonde (HydrolabHach Company, Loveland, Colorado). Measurements were taken in one to five sampling locations per 

pond and averaged across all locations per pond each month. When salinity exceeded the range of the 

minisonde, we measured specific gravity with a hydrometer (Ertco, West Paterson, New Jersey), scaled 

for the appropriate range and corrected for temperature. 

In 2003–04, we conducted bathymetric surveys of 35 ponds (Athearn and others, 2010). We 

used lidar data for 19 ponds for which we lacked bathymetric data (Athearn and others, 2010) and we 

used a combination of bathymetric and lidar data for two ponds. Bathymetric and lidar elevation data 

were interpolated using Inverse Distance Weighting in ArcMap 9.1 (Spatial Analyst, ArcGIS 9.1, ESRI, 

Redlands, California) into a DEM of 25-m resolution. A previous comparison of two ponds found a 0–2 

cm difference between the lidar and bathymetric DEMs (Athearn and others, 2010). However, lidar data 

were not available for regions of the ponds that still contained water, such as borrow ditches or 

channels.

We converted the DEM to a point shapefile, where each point represented an area of 625 m2

. We 

overlaid the bird survey grid and assigned elevation points, where a bird survey grid of 62,500 m2

 (6.25 
30

ha) would have 100 elevation points. For each month and survey grid, we used a combination of staff 

gage readings and bathymetric and lidar elevation surveys to calculate the depth of every elevation point 

per month. We selected depths of 10 cm or below to represent a dry pond surface because of the error 

associated with the elevation measurements. We determined the proportion of a grid cell that was dry 

and therefore available as roosting habitat. 

We digitized islands within ponds from 2009 NAIP (1-m resolution) aerial imagery and adjusted 

island outlines using ground-truthed RTK survey points. We defined an island as land that is exposed 

year-round and is completely surrounded by water. Areas of high elevation that expose when water 

levels decline were not considered islands in the analysis. Using ArcMap 10.1 (ESRI, Redlands, 

California) we overlaid pond grids with island outlines and calculated the number of islands that 

intersected each grid and the proportion of a grid composed of islands. We used the center point of each 

grid to calculate the distance from each grid to the nearest island within the pond and to the nearest pond 

levee. Our analysis included 22 ponds with islands and the number of islands per pond ranged from 1 to 

22 (table 14). We considered Units 1 and 2 of Pond SF2 as two separate ponds. 

Pond and Grid-Scale Analysis

We modeled bird abundance using generalized linear mixed models (GLMM) with a negative 

binomial distribution. We selected this model type because it had a lower mean square error compared 

to models based on an over-dispersed Poisson distribution. Our bird abundance data have a large 

number of zero observations, which the negative binomial distribution is often effective at modeling 

(Warton, 2005). We separately modeled foraging and roosting abundance of seven guilds and five 

species of interest: dabbling ducks, diving ducks, gulls, herons, medium shorebirds, piscivores, small 

shorebirds, American avocets, black-necked stilts, eared grebes, Forster’s terns, and western sandpipers. 

In the first stage of our analysis, we examined the island variables potentially influencing the 

abundance of birds at the pond scale. We developed a base model with variables known or likely to 

affect the abundance of birds within ponds, including water depth and salinity, pond distance to the bay 

and urban areas, time of year (month), pond area, and variation in pond topography (elevation standard 

deviation). Shorebird use of ponds is restricted by water depth (Dias, 2009), whereas dabbling and 

diving ducks have preferred foraging depths (Takekawa and others, 2009). Water salinity limits benthic 

macroinvertebrate (Herbst, 2006; Takekawa and others, 2009) and fish (Marshall and Elliott, 1998) 

community composition, thereby altering the potential prey resources for birds. High tide roost sites for 

shorebirds are limited to areas close to their low tide foraging grounds (Warnock and Takekawa, 1996; 

Dias and others, 2006). Inclusion of these variables in the base model allowed us to focus our analysis 

on the effect of island variables on bird abundance. In order to determine the effects of island attributes 

on abundance, we developed a candidate set of models in which the base model was allowed to improve 

by one or more additional predictor variables, including the presence or absence of islands within the 

pond, the number of islands within the pond, the total island area within the pond, and the ratio between 

island area and pond area. We included the number of islands in every model containing island area or 

area ratio to aid in interpretation of area-related results. Our analysis included 22 ponds with islands and 

37 ponds without islands. Ponds A13 and Units 1 and 2 of Pond SF2 were used as pond without islands 

for data collected prior to island construction. We excluded breached Ponds A6, A19, A20 and A21, and 

excluded data from Ponds A16, A17, E8AE, E8AW, E8X, and E9 after breaching occurred. We also 

excluded data from periods of major construction on particular ponds. 

In the second stage of our analysis, we examined the island variables potentially influencing the 

abundance of birds at the grid scale. We restricted our analysis to ponds with islands. We developed a 

base model with variables known or likely to affect the abundance of birds within grids, including water 
31

depth and salinity, grid distance to the pond levee, whether the grid is located in the pond interior or 

perimeter, time of year (month), grid area, percent of the grid with exposed pond bottom, and variation 

in grid topography. Inclusion of these variables in the base model allowed us to focus our analysis on 

the effect of island variables on bird abundance within ponds that have islands. Our island variables 

included the distance between the grid and the nearest island, total island area within the grid, the ratio 

between island area and grid area, number of islands intersecting the grid, and the presence or absence 

of islands within the grid.

Our base GLMM assumes bird abundance has a log-linear relationship with the base variables 

and that bird abundance varied categorically by month. We included random effects for pond, grid 

(grid-scale analysis), and year to account for potential sources of additional dispersion. We fit all 

models using the glmmADMB package in R statistical software (Fournier and others, 2012; Skaug and 

others, 2012; R Core Team, 2014;). Because of long processing times (up to 15 minutes per model), we 

used a simple Linux utility for resource management (SLURM) to process the 52 sets of models in 

parallel (USGS Core Science Analytics and Synthesis Resource Center in Denver, Colorado). 

Our candidate model set targeted specific hypotheses about the effects of islands. As a reference 

for comparison, we included in our model set the base GLMM (as previously defined) without any 

island-related predictors added to it. To avoid collinear predictors, we did not allow models with any 

combination of two or more of the variables: island presence indicator, number of islands, and distance 

to island. Because island area and area ratio are meaningful only when islands are present, and because

number of islands is more informative than island presence, we explored complex models involving 

island area and area ratio only when number of islands also was in the model. We included models 

containing interaction effects between island variables and month, in order to analyze whether island 

effects varied by month. We also included models containing interaction effects between number of 

islands and other island variables. 

We assembled all combinations of models satisfying the rules of our model set. Each of the 

pond-scale model sets contained 23 models and each of the grid-scale model sets contained 25 models. 

We calculated Akaike’s Information Criterion (AIC) using a customized package (ModelInference

package in R; M.P. Herzog, unpub. data) based on the information-theoretic model selection paradigm 

(Burnham and Anderson, 2002). We identified the top model as the highest ranked model (lowest AIC) 

that was more than 2 AIC units less than the base model to calculate the effects of significant variables 

and their interactions. We evaluated the 95-percent confidence interval of each parameter estimate in the 

top-ranked model of each species and guild. For those estimates with confidence intervals that 

overlapped zero, we used type II ANOVA chi-square tests to determine the statistical significance of the 

variable (Guthery and Brennan 2005; ANOVA function in R package “car”; Fox and Weisberg, 2011). 

For statistically significant island effects interacting with month, we report the direction and magnitude 

of effect of that island variable for each month. For statistically significant effects not interacting with 

month, we report the direction and magnitude of that overall island effect. 
32

Results and Discussion

Pond and Grid-Scale

We conducted 4,056 pond surveys during 10 field seasons between 2002 and 2013 and observed 

88 species of waterbirds, of which 20 were incidentals with fewer than 10 observations (table 15). We 

surveyed as many as 56 ponds per year; however, in any 1 year only a subset of these ponds had a 

complete set of associated environmental data. Thus, the average number of ponds included in the 

dataset each year was 38. Western sandpiper was the most abundant species, followed by dunlin; their 

mean monthly abundances (mean ±SE) peaked in April at 62,516±5,934 and 26,973±3,304 birds, 

respectively. Ruddy duck was the third most abundant species and the most abundant diving duck. The 

mean monthly abundance of ruddy duck peaked in January at 26,125±2,061 birds. Northern shoveler 

was the fourth most abundant species and the most abundant dabbling duck. Their mean monthly 

abundance peaked in October with 15,944±2,712 birds. We modeled foraging and roosting abundance 

of seven guilds and five species of interest, for a total of 52 separate analyses (appendix 1).

Pond Scale-Foraging

For American avocets, the most parsimonious model explaining the foraging abundance 

contained number of islands and had an wi of 0.36 (table 16). The evidence ratio indicated that the top 

model was 1.96 times more likely to explain abundance than the next best model (table A1).The 

parameter estimate was positive and did not overlap zero; ponds with an increasing number of islands 

supported more foraging American avocets (table 17). For black-necked stilts, we determined that the 

most parsimonious model explaining foraging abundance was the base model (wi=0.23), suggesting that 

island parameters had little influence on foraging black-necked stilts (table A2).

The most parsimonious model explaining the foraging abundance of diving ducks (wi =0.17),

eared grebes (wi=1.00), small (wi =1.00) and medium shorebirds (wi= 0.68), and western sandpipers (wi 

= 0.76) contained the variable for presence or absence of islands and the interaction of this variable with

month (table 16). For diving ducks, the evidence ratio indicated that the top model was 1.31 times more 

likely to explain the abundance than the next best model (table A3). For eared grebes and small 

shorebirds, there were no models within 7 ∆AIC of the top model (tables A4 and A5). Medium 

shorebirds and western sandpipers had evidence ratios that indicated their top models were more than 

five times more likely to explain abundance than the next best model (tables A6 and A7). For diving 

ducks, all parameter estimates had 95-percent confidence intervals that overlapped zero and were not 

significant (p > 0.20); thus, we did not find support for island presence or absence affecting the 

abundance of diving ducks. For small and medium shorebirds and western sandpipers, increasing bird 

abundances were associated with the presence of islands within a pond across all months (table 17). The 

same was true for eared grebes, except in February and April when an increasing abundance was 

associated with the absence of islands within a pond (table 17). 

Similarly, the most parsimonious model explaining the foraging abundance of dabbling ducks 

(wi=0.25) included the variable for presence or absence of islands, but did not have an effect by month 

(table 16). The evidence ratio indicated that the top model was 1.26 times more likely to explain 

abundance than the next best model (table A8). The model suggested a higher abundance of foraging 

dabbling ducks in ponds that contained islands (table 17). 

The most parsimonious models explaining the abundance of foraging gulls (wi=0.61) and 

piscivores (wi=0.28) contained number of islands, island area, and the interaction of island area with

month (table 16). The evidence ratios suggested that for gulls and piscivores the top models were 1.82 

and 1.88 times, respectively, more likely to explain abundance than the next best models (tables A9 and 
33

A10). The 95 percent confidence intervals of all parameter estimates overlapped zero, and number of 

islands was insignificant based on Chi-square tests for both gulls (χ2= 0.65, p = 0.42) and piscivores (χ2= 

0.94, p = 0.33). The interaction term was significant in gulls (χ2= 26.03, p = < 0.001) and piscivores (χ2= 

23.87, p < 0.001). Parameter estimates for gulls indicate higher abundances were associated with an 

increasing island area in every month (table 18). In contrast, higher piscivore abundance was associated 

with a decreasing island area in December and February (table 17). Piscivores were most strongly 

associated with increasing island area in early (October and November) and late (March and April) 

winter; however, parameter estimates suggest this guild was more abundant in ponds with lower island 

area during mid-winter months (December–February). 

For Forster’s terns, the most parsimonious model explaining the foraging abundance contained 

number of islands, island area, the interaction of island number and area with each other, by month, and 

as a three-way interaction with month (wi=0.85; table 16). The evidence ratio suggested that the top 

model was 7.54 times more likely to explain the abundance than the next best model (table A11). 

Although, the 95-percent confidence interval of each parameter estimate overlapped zero, the three-way 

interaction estimate was significant (χ2= 69.64, p < 0.001). The abundance of foraging Forster’s terns in 

relation to number of islands and island area, was highly variable by month (table 17). 

The most parsimonious model explaining the abundance of foraging herons (wi=0.39) contained 

number of islands and island-area to pond-area ratio (table 16). The evidence ratio indicated that the top 

model was 1.96 times more likely to explain the abundance than the next best model (table A12). 

However, the 95-percent confidence intervals for parameter estimates overlapped zero and both 

variables were insignificant (χ2= 1.79 and 3.21, p = 0.18 and 0.07, respectively). Therefore, we found 

little support for an island influence on the abundance of foraging herons. 

Pond Scale-Roosting

For American avocets, the most parsimonious model explaining roosting abundance contained 

number of islands, island-area to pond-area ratio, and the interaction of area ratio with month (wi=0.28; 

table 16). The evidence ratio suggested that the top model was 1.49 times more likely to explain the 

abundance than the next best model (table A1). However, the 95-percent confidence interval for 

parameter estimates overlapped zero and no variables were significant (χ2= 1.87, 0.88, and 1.85, p = 

0.17, 0.35, and 0.93, respectively). Therefore, we did not find strong support for island variables 

influencing the abundance of roosting American avocets in ponds. Similarly, the null model for blacknecked stilts (wi=0.23) was within two ΔAIC of the top model (table A2); therefore, we did not find 

support for island parameters influencing the abundance of roosting black-necked stilts in ponds. 

The most parsimonious model explaining the roosting abundance of dabbling ducks (wi=0.25), 

herons (wi=0.58), and medium shorebirds (wi=0.69) contained number of islands, island-area to pondarea ratio, and the interaction of island number with area ratio (table 16). The evidence ratios for 

dabbling ducks indicated that the top model was 1.49 times more likely to explain the abundance than 

the next best model (table A8). The evidence ratios for herons and medium shorebirds indicated that the 

top model was greater than three times more likely to explain the abundance than the next best model 

(tables A6 and A12). All three of these guilds had negative parameter estimates for the interaction 

between number of islands and area ratio (table 18), suggesting a tendency to roost in ponds with fewer 

large islands compared to ponds with more small islands. 

Similarly, the most parsimonious model explaining the roosting abundance of Forster’s terns 

(wi=0.34) and gulls (wi=0.87) included number of islands, island-area to pond-area ratio, and the 

interaction of island number with area ratio with the addition of an interaction between island number 

and month (table 16). The evidence ratio for Forster’s terns suggested that the top model was 1.43 times 
34

more likely to explain the abundance than the next best model (table A11). The evidence ratio for gulls 

suggested that the top model was 8.88 times more likely to explain the abundance than the next best 

model (table A9). For Forster’s terns, October and February through March supported an increasing 

abundance of birds with increasing number of islands (tables 19–20). However, November through 

January supported an increasing abundance of birds with decreasing number of islands (table 18). For 

gulls, number of islands and the interaction number of islands by month were not significant (χ2 = 0.51 

and 7.53, p = 0.47 and 0.27, respectively). For terns and gulls, the interactions between number of 

islands and area ratio were weakly negative and both suggested a slight tendency to roost in areas of 

fewer larger islands (table 18).

The best ranking model for rafting abundance of diving ducks (wi=0.98) and roosting abundance 

of western sandpipers (wi=0.90) contained the variable for presence or absence of islands and the 

interaction of this variable with month (table 16). For diving ducks, no other model was within 7 ∆AIC 

of the top model (table A3). For western sandpipers, the evidence ratio suggested that the top model for 

was 19.78 times more likely to explain the abundance than the next best model (table A7). For small 

shorebirds, the most parsimonious model explaining the roosting abundance contained the variable for 

presence or absence of islands without a month interaction term (wi=0.77; table 16). The evidence ratio 

suggested that the top model was 13.54 times more likely to explain the abundance than the next best 

model (table A5). For diving ducks, western sandpipers, and small shorebirds, a higher roosting or 

rafting abundance was associated with the presence of islands in ponds (table 18). 

We found little support for island variables influencing the abundance of roosting piscivores or 

eared grebes in ponds. Although the top model explaining roosting piscivore abundance (wi=0.19) 

included number of islands and island-area to pond-area ratio (table 16), the 95-percent confidence 

intervals overlapped zero and the variables were not significant (χ2= 1.44 and 2.31, p = 0.23 and 0.13, 

respectively). For eared grebes, the most parsimonious model explaining the roosting abundance was 

the base model (wi=0.20; tables 16 and A4). 

Grid Scale-Foraging

At the grid scale, we determined the most parsimonious model explaining the foraging 

abundance of diving ducks (wi=1.00), gulls (wi=0.99), and medium shorebirds (wi=0.39) contained grid 

distance to island and the interaction of distance by month (table 21). For diving ducks and gulls, no 

models were within 7 ∆AIC of the top model (tables A13 and A14). Higher abundance was associated 

with grids closer to islands for all months except October for diving ducks and February for gulls (table

22). For medium shorebirds, the evidence ratio indicated the top model was 1.30 times likely to explain 

abundance than the next best model (table A15). The 95 percent confidence interval of the interaction 

parameter estimate overlapped zero and was not significant (χ2= 9.06, p = 0.17); however, a higher 

abundance of foraging medium shorebirds was associated with grids closer to islands (table 22, fig. 13). 

Similarly, the most parsimonious model explaining the abundance of American avocets contained grid 

distance to island without the interaction term (wi=0.65; table 21, fig. 13). The evidence ratio indicated 

that the top model was 3.09 times more likely to explain abundance than the next best model (table

A16). A higher abundance of foraging avocets was associated with grids closer to islands (table 22, fig. 

13). 
35

For black-necked stilts (wi=0.31) and piscivores (wi=0.23), the most parsimonious model 

explaining the foraging abundance contained number of islands and island-area to grid-area ratio, as 

well as the interaction of island number with ratio for stilts and the interaction of island number with

month for piscivores (table 21). The evidence ratios indicated that the top models were 1.14 and 1.11 

times more likely to explain the abundance of stilts and piscivores, respectively, than the next best 

models (tables A17 and A18). For black-necked stilts, the 95 percent confidence interval of island-area

to grid-area ratio parameter estimate overlapped zero but the variable was significant (χ2= 3.64, p = 

0.06). For piscivores, the 95 percent confidence interval of the parameter estimate for the interaction of 

number of islands with month overlapped zero but the variable was significant (χ2= 34.27, p < 0.001). 

During all months except October and April, an increasing abundance of piscivores was associated with 

a decreasing number of islands per grid (table 22). However, an increasing abundance of piscivores was 

associated with an increasing island-area to grid-area ratio (table 22). 

The most parsimonious model explaining the foraging abundance of dabbling ducks (wi=0.72) 

and herons (wi=0.39) contained presence or absence of islands and the interaction of this variable with

month (table 21). For dabbling ducks, the evidence ratio indicated that the top model was 6.05 times 

more likely to explain the abundance than the next best model (table A19). Increased dabbling duck 

abundance was associated with the presence of islands in grids during all months except March and 

April (table 22). For herons, the evidence ratio indicated that the top model was 3.65 times more likely 

to explain the abundance than the next best model (table A22). Increased heron abundance was 

associated with the presence of islands in grids during all months except February (table 22). 

For eared grebes, the most parsimonious model explaining the foraging abundance number of 

islands and island area (wi=0.34; Table 21). The evidence ratio indicated that the top model was 2.68 

times likely to explain abundance than the next best model (table A21). The 95-percent confidence 

interval for number of islands overlapped zero and the variable was not significant (χ2= 2.61, p = 0.10). 

A decreasing island area supported an increasing abundance of foraging eared grebes (table 22). 

However, the null model was the third highest ranked and within three ΔAIC of the top model. 

Therefore, we have weak support for island parameters influencing the foraging abundance of eared 

grebes at the grid scale.

For Forster’s terns, the most parsimonious model explaining the foraging abundance contained 

number of islands, island area, and the interactions of island number with area and island number with 

month (wi=0.34; Table 21). The evidence ratio indicated that the top model was 2.36 times more likely 

to explain abundance than the next best model (table A22). The 95-percent confidence intervals for 

number of islands and the interaction of that variable with month overlapped zero but were significant 

(χ2= 199.72 and 55.17, respectively, p < 0.001 for both). For all months except October, an increasing 

abundance was associated with a decreasing number of islands per grid (table 22). 

The most parsimonious model explaining the foraging abundance of small shorebirds contained 

number of islands, island area, and the interaction of island number and area with each other and with 

month (wi=0.38; Table 21). The evidence ratio indicated that the top model was 2.59 times more likely 

to explain abundance than the next best model (table A23). The most parsimonious model explaining 

the roosting abundance of western sandpipers included the same variables as well as a three-way 

interaction between island number, area, and month (wi=0.70; Table 17). The evidence ratio indicated 

that the top model was 5.53 times more likely to explain abundance than the next best model (table

A24). For small shorebirds, only the interaction of number of islands with month and number of islands 

with island area were significant (χ2= 17.58 and 6.90, p = 0.01 and 0.01, respectively). December, 

February, and March supported a higher abundance of small shorebirds in grids with a decreasing 

number of islands, compared to October, November, January, and April, which supported a higher 
36

abundance in grids with an increasing number of islands (table 22). For western sandpipers, number of 

islands (χ2= 87.07, p < 0.001), island area (χ2= 32.10, p < 0.001), and the interactions of month with 

number of islands (χ2= 392.60, p < 0.001) and month with island area (χ2= 567.09, p < 0.001) were 

significant. Across all months except February and March grids with an increasing number of islands 

supported an increasing abundance (table 22). Across all months there was strong support for increasing 

island area per grid supporting an increasing abundance of western sandpipers (table 22). 

Grid Scale-Roosting

The most parsimonious model explaining the roosting abundance of American avocets 

(wi=0.55), piscivores (wi=0.65), herons (wi=0.53), and medium shorebirds (wi=0.93) contained the 

variable for presence or absence of islands (table 21). For American avocets and herons, the evidence 

ratio indicated that the top model was 1.3–1.5 times more likely to explain the abundance than the next 

best model (tables A16 and A20). For piscivores and medium shorebirds, the evidence ratio indicated 

that the top model was 5.05 and 20.31 times more likely to explain the abundance, respectively, than the 

next best model (tables A18 and A15). An increasing abundance of birds was associated with the 

presence of islands in grids (table 23). 

Similarly, the most parsimonious model explaining the roosting abundance of black-necked stilts 

(wi=0.63), dabbling ducks (wi=0.98), Forster’s terns (wi=0.42), small shorebirds (wi=0.29), and western 

sandpipers (wi=0.51) contained the variable for presence or absence of islands, as well as the interaction 

of this variable with month (table 21). For stilts, terns, and western sandpipers, the evidence ratio 

indicated that the top model was 1.77–3.54 times more likely to explain abundance, respectively, than 

the next best model (tables A17, A22, and A24). No other model was within 7 ΔAIC of the top model 

for dabbling ducks (table A19). For small shorebirds, the evidence ratio of 1.01 suggested that the next 

best model (wi=0.29), including number of islands, island-area-to-pond-area ratio, and interactions, 

might also explain the abundance (table A23). For Forster’s terns, the 95-percent confidence intervals of 

parameter estimates overlapped zero and therefore we did not find support for island variables affecting 

abundance at the grid scale. An increasing abundance was supported by the absence of islands for blacknecked stilts in February, for dabbling ducks in April, and for western sandpipers in October, 

November, and February (table 23). For small shorebirds, an increasing abundance was supported by 

the presence of islands across all months (table 23). 

The most parsimonious model explaining the roosting abundance of eared grebes (wi=0.68) and 

gulls (wi=0.99) contained grid distance to island and the interaction of distance with month (table 21). 

For eared grebes, the evidence ratio indicated that the top model was 3.95 times more likely to explain 

abundance than the next best model (table A21). No other model was within seven ΔAIC of the top 

model for gulls (table A14). For eared grebes, the 95-percent confidence intervals of parameter 

estimates overlapped zero and the variables were not significant (χ2= 0.97 and 8.51, p = 0.33 and 0.20, 

respectively); therefore, we did not have support for island variables influencing the abundance of eared 

grebes. For gulls, an increasing abundance was supported by a decreasing distance to island (table 23). 
37

In contrast to all other species, the most parsimonious model explaining the rafting abundance of 

diving ducks contained number of islands, island area, the interaction of island number and area with 

each other and with month, and a three-way interaction between number, area, and month (wi=0.68; 

Table 21). The 95-percent confidence intervals for all parameter estimates overlapped zero. Number of 

islands (χ2= 4.02, p = 0.045), the interaction of island number with month (χ2= 3670.72, p < 0.001), and 

the interaction of island area with month (χ2= 16932.52, p < 0.001) were significant whereas island area 

(χ2= 1.96, p = 0.16), the interaction between island number and area (χ2= 0.00, p = 0.97), and a threeway interaction between number, area, and month (χ2= 4.11, p = 0.66) were not significant. The 

influence of island variables on abundance varied by month (table 23). In most months, fewer large 

islands were associated with an increasing abundance of rafting diving ducks (table 23). 

Summary

Overall, we determined that the presence and area of islands clearly influenced abundances of 

wintering birds at the pond scale. Abundance of four out of five species and all seven guilds that we 

studied were associated with island features either during foraging or roosting (see summaries of island 

effects at the pond scale in tables 19 and 20). In most instances the presence of islands at the pond scale 

either increased or had no measureable effect on abundances of foraging and roosting birds (tables 19 

and 20). 

Abundance of foraging avocets was positively associated with an increasing number of islands at 

the pond scale (table 19). Salt ponds and other impoundments are commonly used by wintering avocets 

(Stenzel and others, 2002; Rintoul and others, 2003; Mellink and Riva, 2005), and within these habitats 

avocets occur in shallow waters between 8.5 and 17 cm deep (Boettcher and others, 1995; Weber and 

Haig, 1996). Managed ponds containing several small islands may provide increased foraging 

opportunities associated with access to a gradient of depths along island edges that are within this 

preferred range. Managed ponds comprise as much as 60 percent of black-necked stilt core use areas 

(Ackerman and others, 2007; Hickey and others, 2007;), and we determined that ponds with islands had 

increased abundances of foraging stilts (tables 19 and 20). 

For all species and guilds, we found great variation in response to island parameters across study 

months (tables 19 and 20). These temporal differences may represent change in use of ponds with 

islands during spring and autumn migration or during more extreme mid-winter tides. Additionally, the 

amount of shallow or exposed pond bottom can vary by season, rainfall, and management actions within 

each pond and can cause island habitat to be more important at different times within or across years. 

We determined that island variables had no measurable influence at the pond scale on the 

number of roosting American avocets, black-necked stilts, eared grebes, or piscivores. Alternative 

roosting habitat does exist within the project ponds, and may be important for some of these species or 

guilds. In addition to islands, waterbirds roost on pond levees, on shallow or exposed pond bottom, and 

on wooden structures within the pond. Although there is more levee area compared to island area 

available for roosting waterbirds, we have not determined what levee features support roosting birds and 

what levee area may be reduced with future restoration actions. Thus, although islands may not 

currently influence the roosting abundance of some species or guilds, they may become more important 

in the future as management and restoration actions change the availability of alternative roosting 

habitat. 
38

While pond-scale analyses help identify the role of islands in attracting birds to a pond, gridscale analyses provide the opportunity to evaluate how island features within a pond influence avian 

abundances. At the grid scale, we determined that the presence of islands increased roosting or foraging 

abundance for four of five species and five of seven guilds that we evaluated in this study (see 

summaries of island effects at the grid scale in tables 24 and 25). For eared grebes, a species associated 

with open water in hyperhaline ponds (Athearn and others, 2010), abundance decreased with increasing 

island area within a grid. This may be related to an increase in foraging opportunities for these species 

in the deeper, open-pond areas. 

Overall, effects of island area and the island-area to pond-area ratio were evident for fewer 

species at the grid scale than at the pond scale (tables 20, 24, and 25). For foraging avocets, gulls, and 

medium shorebirds, distance to nearest island had a positive effect such that bird abundance increased in 

grids closer to islands (tables 24 and 25). For diving ducks, the effect of islands had a slight parabolic 

shape, with a positive effect on abundance close to islands and a much stronger positive effect at 

distances equal to or greater than 1,500 m from islands (fig. 13). These taxa could have been responding 

to different features associated with islands. For example, islands are often adjacent to borrow ditches 

where sediment has been removed to form the island. The slight increase in diving ducks observed near 

islands may be a response to increased foraging opportunities in these deeper water areas close to some 

islands; however, foraging opportunities away from islands clearly draw larger numbers of ducks. In 

contrast, avocets and medium shorebirds may be responding to sloping island shores and increased areas 

of shallow water within their preferred foraging depths (Boettcher and others, 1995), or to the proximity 

of suitable island roosting areas to their foraging area (, Dias and others, 2006; Rosa and others, 2006; 

tables 24 and 25). 

After accounting for variables, including salinity, depth, and area, known to influence the 

abundance of birds, we determined that island variables influenced the abundance of roosting and 

foraging birds at both pond and grid scales within SBSP Restoration Project area. Across ponds, the 

presence of increasing number of islands had a positive influence on the roosting and foraging 

abundance of most taxa studies. For several species or guilds, such as gulls, fewer large islands were 

associated with an increasing abundance. Within ponds, distance to island was an important variable, 

such that grids with islands and grids closer to islands supported a higher abundance of most species or 

guilds. Therefore, dispersing islands across individual ponds would increase the number of grids closer 

to islands, and may enhance bird abundance. Across the study ponds, most islands occupied only a 

small proportion of a pond or grid and our sample size of larger islands and larger island-area to pondor grid-area ratios was limited. Our data set included ponds with a range of between 1 and 22 islands, 

covering up to 12 percent of the pond, and in most ponds island area occupied < 1 percent of pond area. 

There is likely an upper limit to the number or area of islands that is beneficial to wintering birds; 

however, estimating this number would require a larger range of island number and sizes. Our results 

demonstrated a positive influence of islands on wintering bird abundance and suggested that additional 

islands may benefit most species or guilds.

Overall, from our analysis of historical salt pond bird abundance data, we have identified several 

island variables that influence the abundance of wintering birds across and within ponds: 

1. At the pond scale, we determined that the presence of islands within ponds increased the overall 

abundance of most bird species within ponds. Therefore, more ponds with islands will likely 

have an overall positive benefit to bird abundance. The influence of islands varied by species 

and guild, but presence of islands did not have a negative influence on the abundance of any 

species or guild. 
39

2. For foraging birds at the pond scale, islands had a positive influence on the abundance of 

American avocets, eared grebes, Forster’s terns, western sandpipers, dabbling ducks, gulls, small 

and medium shorebirds, and piscivores. Of these species and guilds, island area positively 

influenced the abundance of gulls, Forster’s terns, and piscivores, whereas number of islands 

positively influenced the abundance of American avocets and black-necked stilts. Islands did not 

influence the abundance of foraging diving ducks or herons. 

3. For roosting birds at the pond scale, islands had a positive influence on the abundance of 

Forster’s terns, western sandpipers, dabbling ducks, diving ducks, gulls, herons, and small and 

medium shorebirds. For all taxa except small shorebirds, island-area to pond-area ratio had a 

positive influence on the abundance. Islands did not influence the abundance of roosting 

American avocets, black-necked stilts, eared grebes, and piscivores. 

4. At the grid scale, the abundance of most birds was greater in areas with islands. Once birds 

selected ponds that contain islands, their abundance within the pond is influenced by the spatial 

location of islands within the ponds. For most birds, islands had a positive influence on the 

abundance of birds within ponds, and islands only had a negative influence on the abundance of 

foraging eared grebes. 

5. For foraging birds at the grid scale, islands had a positive influence on the abundance of 

American avocets, black-necked stilts, Forster’s terns, western sandpipers, dabbling ducks, 

diving ducks, gulls, herons, piscivores, and small and medium shorebirds. The abundance of 

black-necked stilts and piscivores was positively influenced by island-area to pond-area ratio, 

whereas the abundance Forster’s terns and small shorebirds was positively influenced by island 

area. 

6. Islands that are scattered across the pond rather than clustered together may increase the 

preferred foraging area of many birds. The abundance of foraging American avocets, gulls, 

diving ducks, and medium shorebirds was greatest in grids closer to islands, though diving ducks 

were most abundant farther from islands. The abundance of eared grebes was negatively 

influenced by island area. 

7. For roosting birds at the grid scale, islands had a positive influence on the abundance of 

American avocets, black-necked stilts, western sandpipers, dabbling ducks, diving ducks, 

herons, piscivores, and small and medium shorebirds. The abundance of diving ducks was 

positively influenced by island area whereas the abundance of gulls increased in grids closer to 

islands. Islands did not influence the abundance of eared grebes and Forster’s terns. 

Acknowledgments 

This study was supported by the Resources Legacy Fund, South Bay Salt Pond Restoration 

Project, USGS Priority Ecosystems Science Program, USGS Western Ecological Research Center, and 

CSU COAST. We thank the Don Edwards San Francisco Bay National Wildlife Refuge Complex (Anne 

Morkill, Eric Mruz, Cheryl Strong), Eden Landing Ecological Reserve (John Krause), South Bay Salt 

Pond Restoration Project (Laura Valoppi and John Bourgeois), and Resources Legacy Fund (Aaron 

O’Callaghan) for logistical and project support. Guidance for the Pond SF2 wintering bird study was 

provided by S. Shaffer and L. Bonachea as part of S. Moskal’s Master’s Thesis. J. Falgout, J. Gordon, 

and B. Williams at CSAS in Denver provided integral assistance in using the SLURM. We thank N. 

Athearn and L. Brand for winter bird project coordination, J. Shinn for data management and K. 

Spragens for helpful discussion. We thank the many field assistants who participated in the nesting bird 
40

studies (Collin Eagles-Smith, Garth Herring, Tabitha McKinney, Trevor Watts, Jessica LaCoss, Nina 

Hill, Jarred Barr, Kristen Boysen, Sarah Flaherty, Erik Nass, Nicolette Roach, Kim Sawyer, and Camille 

Yabut) and winter bird surveys (A. Anderson, T. Barnum, K. Barry, M. Bauman, A. Bibian, L. Bloch, J. 

Bluso-Demers, K. Brailsford, V. Bui, M. Carroll, W. Chan, I. Clearwater, L.A. Curry, C. Dagget, L. 

DeMais, J. Despot, D. Drolette, S. Demers, E. French, E. Garfinkle, D. Gaube, P. Gibson, K. 

Goodenough, T. Graham, J. Green, D. Haines, B. Hattenbach, K. Henderson, B. Hess, K. Hirsch, L. 

Hollander, K. Kapantais, C. Kranz, J. Liechty, K. Lovett, S. Macias, J. MacLean, S. Major, A. 

Meckstroth, D. Monié, D. Morgan, H. Mounce, A. Murphy, S. Page, A. Perry, S. Piotter, C. Potter, C. 

Reyes, A. Rowan, M. Schaap, A. Schultz, J. Shinn, A. Shults, S. Spring, M. Stafford, C. Strong, D. 

Tsao-Melcer, L. Terrazas, R. Unks, B. Wensky, and A. Westhoff). 

Unpublished References

Bluso-Demers, J.D., Ackerman, J.T., and Takekawa, J.Y., Habitat selection by Forster’s terns at 

multiple spatial scales in San Francisco Bay—The importance of salt ponds. Unpublished manuscript. 

Rocha, A.R., Woo, I., Takekawa, J.Y., Davis, J.D., and Lovvorn, J.R., Temporal variation of 

macroinvertebrate carrying capacity for shorebirds on an estuarine mudflat. Unpublished manuscript. 

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Figure 1. Salt pond locations in South San Francisco Bay are grouped into several pond complexes within the Don 

Edwards San Francisco Bay National Wildlife Refuge (Alviso, Moffet, Newark, Ravenswood) and the Eden Landing 

Ecological Reserve. Ponds investigated in this report are denoted with black hatching. 
46

Figure 2. Aerial view of Ravenswood Pond SF2 with constructed islands, South San Francisco Bay, California. 
47

Figure 3. Waterbird nest abundance was greatest within ponds very close (<1 kilometer [km]) or far (4–5 km) from 

San Francisco Bay, California. AMAV=American avocet, BNST=black-necked stilt, and FOTE=Forster’s terns.
48

Figure 4. Waterbird nest success was greatest within ponds (a) very close (<1 kilometer) to San Francisco Bay, 

and (b) in ponds 1,200–1,700 hectares (ha) in size. AMAV=American avocet, BNST=black-necked stilt, and 

FOTE=Forster’s terns.
49

Figure 5. Waterbird nest abundance was (a) greater on islands located closest (<1 kilometer) to San Francisco 

Bay, (b) greater on linear shaped islands (positive residuals) compared to rounded islands (negative residuals), and 

(c) increased as island distance to nearest surrounding pond levee increased. AMAV=American avocet, 

BNST=black-necked stilt, and FOTE=Forster’s terns.
50

Figure 6. Waterbird nest success (a) decreased as island distance to San Francisco Bay increased, and (b) 

increased as island distance to surrounding pond levee increased to approximately 150 m, and decreased 

thereafter. AMAV=American avocet, BNST=black-necked stilt, and FOTE=Forster’s terns.
51

Figure 7. American avocet probability of nesting on island grid cells in South San Francisco Bay in 2011 (closed 

markers) and 2012 (open markers). (a) increased as elevation increased up to a peak at approximately 0.8 m, 

decreasing thereafter, (b) increased as distance to water increased up to a peak at approximately 7 m, decreasing 

thereafter, (c) increased as slope increased up to a peak at approximately 15 degrees; (d) was relatively constant 

among aspect orientations (values 180–360 degrees were converted to 0–180 degrees for presentation); (e) 

decreased as the number of bordering cells used by avocets (circles) increased, yet increased as the number of 

bordering cells used by avocet and terns (squares) increased up to a peak of 5 cells, decreasing thereafter, and (f) 

decreased as the number of avocet nests in bordering cells (circles) increased, yet increased as the number of 

avocet and tern nests in bordering cells (squares) increased up to a peak of 11 nests, decreasing thereafter. Each 

variable effect relationship with probability of nesting is displayed at the mean values for all other variables.
52

Figure 8. Forster’s tern probability of nesting on island grid cells in South San Francisco Bay in 2011 (closed 

markers) and 2012 (open markers) (a) increased with greater elevation, (b) increased with increasing distance to 

the water’s edge up to a peak at approximately 2 meters, decreasing thereafter, (c) decreased as slope increased 

beyond approximately 10 degrees; (d) was slightly lower for grids with north-facing versus south-facing aspects 

(values 180–360 degrees were converted to 0–180 degrees for presentation); (e) increased as more bordering grid 

cells were used by nesting terns (circles) and nesting terns and avocets (squares), and (f) increased as the number 

of tern (circles), and avocet and tern (squares) nests in bordering cells increased up to a peak of 10 tern nests and 

12 avocet and tern nests, decreasing thereafter. Each variable effect relationship with probability of nesting is 

displayed at the mean values for all other variables.
53

Figure 9. The proportion of each nest microhabitat grid cell and paired random site microhabitat grid cell in which 

vegetation was the dominant cover type among (a) American avocets and (b) Forster’s terns. More (30 percent) 

avocet nest microhabitats had vegetation cover in the center grid cell (the cell where the nest bowl was located) 

compared to paired random site microhabitats (21 percent). More avocet nest microhabitat cells north (28 percent) 

and east (24 percent) of the nest bowl, also exhibited vegetation cover than corresponding cells of paired random 

site microhabitats (22 and 21 percent, respectively). A greater number of tern nests had vegetation cover within 

each of the nine microhabitat grid cells relative to paired random sites. Shading denotes microhabitat cells in which 

a significantly (p ≤ 0.05) greater number of nest sites had vegetation cover than paired random sites. Darker 

shading indicates greater differences between microhabitat cells at nest sites and paired random sites.
54

Figure 10. Total bird abundance observed at a) high tide and b) low tide on Pond SF2 islands by season for the 

survey years 2010–11, South San Francisco Bay, California. 
55

Figure 11. Temporal trend in waterbird abundance on Pond SF2 for (A) low and (B) high tides across years, South 

San Francisco Bay, California. Data are displayed by habitat type.
56

Figure 12. Mean bird abundance on Pond SF2 at high tide across all islands, South San Francisco Bay, California. 

Error bars represent 95-percent confidence intervals.
57

Figure 13. Abundance of foraging American avocets and medium shorebirds was greater in grids closer to islands, 

while diving duck abundance increased with increased distance from islands in South San Francisco Bay ponds, 

California. 
58

Table 1. Attributes of the 22 ponds modeled for waterbird nest abundance and nest success in South San 

Francisco Bay from historical nesting data collected in 2005–2013.

[Total number of islands: Number of islands in the pond. Number of islands nested on: Number of islands on which 

avocet, stilt, or tern nested in one or more years. Years included: Maximum number of years for which we had nesting data 

for one or more species. SF, San Francisco; ha, hectare; m, meter] 

Pond

Total 

number 

of 

islands

Number 

of 

islands 

nested 

on

Total 

island 

area 

(ha) 

Pond 

area 

(ha) 

Island 

area to 

pond 

area 

ratio

Distance 

to

SF Bay (m)

Years 

included

Total 

number of 

Avocet 

nests

Total 

number 

of

Stilt 

nests

Total 

number 

of

Tern 

nests

A1 3 1 0.10 1,136 0.09 66 8 196 4 555

A12 10 7 0.44 1,278 0.35 4,177 6 41 0 0

A13 5 5 0.09 1,111 0.08 4,271 2 62 0 0

1

A16 5 5 0.29 1,007 0.28 5,440 7 445 38 1,049

A17 1 1 0.12 548 0.23 5,477 3 238 1 0

A2W 3 3 0.06 1,778 0.03 27 7 314 85 1,442

A7 6 6 0.02 1,094 0.01 1,413 8 130 0 820

A8 1 1 0.01 1,673 0.01 3,735 4 156 2 496

AB1 1 1 0.04 623 0.07 8 7 167 37 1,047

AB2 28 12 0.67 737 0.90 5 7 131 6 530

E10 4 1 0.38 873 0.43 49 3 26 8 0

2

E10X 2 1 0.16 215 0.76 49 1 8 0 0

E2 5 4 0.88 2,773 0.32 24 5 136 0 26

E6A2 2 1 0.23 1,305 0.18 3,437 1 8 0 0

E7 10 4 0.26 883 0.29 1,793 4 41 0 103

3

N4/N5 1 1 0.14 2,168 0.07 25 5 88 0 0

N4A 11 3 0.15 1,223 0.12 1,197 7 103 11 0

N4AB 9 7 0.21 964 0.21 25 6 130 32 7

3

N6/N7 3 3 0.75 1,928 0.39 408 1 13 0 0

N8/N93 10 2 0.50 1,018 0.49 1,150 2 79 17 0

R1 4 3 0.52 1,831 0.29 10 6 352 7 59

SF2 30 28 4.84 997 4.85 68 2 158 0 0 1

Sixteen new islands were constructed in winter of 2013 at Pond A16 as enhancement to increase nesting and foraging value 

for waterbirds, bringing the total number of islands in Pond A16 to 20 (one of the five historical islands was removed).

However, incomplete flooding of this pond prevented these islands from fully functioning as islands during the 2013 

breeding season. As a result, we omitted 2013 data from Pond A16 from all analyses. 2

Data not used in nest success analyses. 3

Originally two separate ponds, this combined pond was created when the dividing levee was cut to make island habitat.
59

Table 2. Model selection results for factors affecting waterbird nest abundance n 22 ponds in South San Francisco 

Bay from historical nesting data collected in 2005–13. 

[All models with a cumulative model weight of 90 percent are shown. DistBay: distance to San Francisco Bay. Islands: 

number of islands within the pond. IslArea:PondArea: the ratio of island area to pond area. PondArea: total area of the pond.

IslandArea: total island area of all the islands within a pond]

Model ka -2LogL AICc

b ∆AICcc wd Evidence 

ratioe

Species + DistBay + DistBay2 6 6 646.1 0.00 0.11 1.00

Species + DistBay + DistBay2 + Islands 7 7 641.8 0.16 0.10 1.08

Species + DistBay + DistBay2 + IslArea:PondArea + 

IslArea:PondArea2

8 8 636.8 0.22 0.10 1.11

Species + Islands 5 5 650.1 0.22 0.10 1.12

Species + Islands + Islands2 6 6 646.9 0.79 0.07 1.49

Species + DistBay + DistBay2 + PondArea 7 7 643.6 1.91 0.04 2.59

Species 4 4 655.3 2.03 0.04 2.76

Species + Islands + IslArea:PondArea 6 6 648.8 2.76 0.03 3.97

Species + Islands + IslandArea 6 6 649.1 3.09 0.02 4.68

Species + DistBay + DistBay2 + IslandArea + IslandArea2 8 8 639.7 3.13 0.02 4.77

Species + DistBay + Islands 6 6 649.3 3.26 0.02 5.09

Species + DistBay + DistBay2 + IslArea:PondArea 7 7 645.1 3.45 0.02 5.63

Species + DistBay + DistBay2 + PondArea + PondArea2 8 8 640.1 3.48 0.02 5.68

Species + DistBay + Islands + Islands2 7 7 645.3 3.66 0.02 6.23

Species + Islands + PondArea 6 6 649.8 3.75 0.02 6.52

Species + DistBay + DistBay2 + IslandArea 7 7 645.5 3.87 0.02 6.93

Species + DistBay + Species 5 5 653.8 3.90 0.02 7.02

Species + PondArea 5 5 653.8 3.93 0.01 7.15

Species + DistBay + DistBay2 + Islands + Islands2 8 8 640.9 4.30 0.01 8.58

Species + IslArea:PondArea + IslArea:PondArea2 6 6 650.4 4.31 0.01 8.63

Species + DistBay + DistBay2 + Islands + PondArea 8 8 640.9 4.34 0.01 8.76

Species + DistBay + DistBay2 + IslandArea + IslandArea2 + 

PondArea

9 9 635.2 4.56 0.01 9.76

Species + Islands + Islands2 + IslandArea 7 7 646.5 4.79 0.01 10.99

Species + Islands + Islands2 + PondArea 7 7 646.5 4.82 0.01 11.12

Species + Islands + Islands2 + IslArea:PondArea 7 7 646.5 4.83 0.01 11.16

Species + DistBay + DistBay2 + Islands + IslArea:PondArea 8 8 641.5 4.95 0.01 11.91

Species + DistBay + DistBay2 + Islands + IslandArea 8 8 641.6 5.00 0.01 12.20

Species + IslArea:PondArea 5 5 654.9 5.03 0.01 12.39

Species + IslandArea 5 5 655.2 5.26 0.01 13.87

Species + DistBay + DistBay2 + IslArea:PondArea + 

IslArea:PondArea2 + PondArea

9 9 636.2 5.53 0.01 15.84

Species + DistBay + PondArea 6 6 651.7 5.66 0.01 16.98

Species + DistBay + DistBay2 + IslArea:PondArea + 

IslArea:PondArea2 + Islands

9 9 636.5 5.81 0.01 18.29

Species + DistBay + DistBay2 + IslArea:PondArea + 

IslArea:PondArea2 + IslandArea

9 9 636.8 6.13 0.00 21.48

Species + DistBay + DistBay2 + IslandArea + PondArea 8 8 643.0 6.37 0.00 24.19

a

The number of parameters in the model including the intercept and variance.

b

Akaike’s Information Criterion corrected for small sample size.

c

The difference in the AICc values of the current model and the model with the lowest AICc. d

Akaike model weight. The likelihood of the model given the data, relative to other models in the candidate set. e

The weight of evidence that the model with the lowest AICc value is better than the current model
60

Table 3. Model selection results for factors affecting waterbird nest success in 20 ponds in South San Francisco 

Bay from historical nesting data collected in 2005-13. 

[All models with a cumulative model weight of 90 percent are shown. DistBay: distance to San Francisco Bay. Islands: 

number of islands within the pond. IslArea:PondArea: the ratio of island area to pond area. PondArea: total area of the pond.

IslandArea: total island area of all the islands within a pond]

Model ka -2LogL AICc

b ∆AICcc wd Evidence 

ratioe

DistBay + DistBay2 + Islands + PondArea + 

PondArea2

7 -3.6 19.7 0.00 0.21 1.00

DistBay + DistBay2 + IslandArea + IslandArea2 6 1.3 19.8 0.11 0.20 1.06

DistBay + DistBay2 + PondArea + PondArea2 6 2.0 20.5 0.79 0.14 1.48

DistBay + DistBay2 + PondArea + PondArea2 + 

IslArea:PondArea

7 -0.9 22.5 2.77 0.05 3.99

DistBay + DistBay2 + IslandArea + PondArea + 

PondArea2

7 -0.4 23.0 3.28 0.04 5.16

DistBay + DistBay2 + PondArea + PondArea2 +

IslArea:PondArea + IslArea:PondArea2

8 -6.0 23.1 3.38 0.04 5.42

DistBay + DistBay2 + IslArea:PondArea + 

IslArea:PondArea2

6 4.9 23.4 3.68 0.03 6.30

DistBay + DistBay2 + IslandArea + 

IslArea:PondArea + IslArea:PondArea2

7 0.7 24.0 4.35 0.02 8.80

DistBay + DistBay2 + IslandArea + IslandArea2 + 

IslArea:PondArea + IslArea:PondArea2

8 -4.7 24.4 4.73 0.02 10.64

DistBay + DistBay2 + IslandArea + IslandArea2 + 

PondArea

7 1.1 24.5 4.77 0.02 10.86

DistBay + DistBay2 + IslandArea + IslandArea2 + 

PondArea + PondArea2

8 -4.6 24.5 4.78 0.02 10.91

DistBay + DistBay2 + IslandArea + IslandArea2 + 

IslArea:PondArea

7 1.3 24.6 4.90 0.02 11.59

DistBay + DistBay2 + Islands + IslandArea + 

IslandArea2

7 1.3 24.7 4.98 0.02 12.06

DistBay + DistBay2 + Islands + Islands2 + PondArea 

+ PondArea2

8 -3.9 25.2 5.54 0.01 15.96

DistBay + DistBay2 + Year 11 -29.7 25.3 5.59 0.01 16.36

DistBay + DistBay2 + Islands + IslArea:PondArea + 

IslArea:PondArea2

7 2.0 25.4 5.66 0.01 16.95

DistBay + DistBay2 + Islands + IslandArea + 

PondArea + PondArea2

8 -3.7 25.4 5.74 0.01 17.64

DistBay + DistBay2 + Islands + IslArea:PondArea + 

PondArea + PondArea2

8 -3.7 25.4 5.74 0.01 17.64

DistBay + DistBay2 + Islands 5 11.5 25.8 6.06 0.01 20.70

DistBay + DistBay2 + PondArea + 

IslArea:PondArea + IslArea:PondArea2

7 2.9 26.3 6.56 0.01 26.58

a

The number of parameters in the model including the intercept and variance.

b

Akaike’s Information Criterion corrected for small sample size.

c

The difference in the AICc values of the current model and the model with the lowest AICc. d

Akaike model weight. The likelihood of the model given the data, relative to other models in the candidate set.

e

The weight of evidence that the model with the lowest AICc value is better than the current model
61

Table 4. Model selection results for factors affecting waterbird nest abundance on 100 islands in ponds in South 

San Francisco Bay from historical nesting data collected in 2005-13. 

[All models with a cumulative model weight of 90 percent are shown. DistBay: distance to San Francisco Bay. Islands: 

number of islands within the pond. IslArea:PondArea: the ratio of island area to pond area. PondArea: total area of the pond.

IslandArea: total island area of all the islands within a pond]

Model ka -2LogL AICcb ∆AICc

c wd Evidence 

ratioe

Species + DistBay + DistBay2 + IslandShape + IslandShape2 + 

DistLevee

10 1415.0 1437.5 0.00 0.48 1.00

Species + DistBay + DistBay2 + IslandShape + IslandShape2 + 

DistLevee + IslandArea

11 1414.8 1439.8 2.26 0.16 3.10

Species + DistBay + DistBay2 + IslandShape + IslandShape2 + 

DistLevee + DistLevee2

11 1415.0 1440.0 2.52 0.14 3.53

Species + DistBay + DistBay2 + IslandShape + IslandShape2 + 

IslandArea + IslandArea2 + DistLevee

12 1414.7 1442.3 4.83 0.04 11.17

Species + DistBay + DistBay2 + IslandShape + IslandShape2 + 

DistLevee + DistLevee2 + IslandArea

12 1414.7 1442.3 4.84 0.04 11.26

Species + DistBay + DistBay2 + IslandShape + IslandShape2 9 1422.7 1442.7 5.19 0.04 13.36

a

The number of parameters in the model including the intercept and variance.

b

Akaike’s Information Criterion corrected for small sample size.

c

The difference in the AICc values of the current model and the model with the lowest AICc. d

Akaike model weight. The likelihood of the model given the data, relative to other models in the candidate set.

e

The weight of evidence that the model with the lowest AICc value is better than the current model.
62

Table 5. Model selection results for factors affecting waterbird nest success on 44 islands in ponds, from historic 

nesting data collected in 2005-13, South San Francisco Bay, California. 

[All models with a cumulative model weight of 90 percent are shown.DistBay: distance to San Francisco Bay. Islands: 

number of islands within the pond. IslArea:PondArea: the ratio of island area to pond area. PondArea: total area of the pond.

IslandArea: total island area of all the islands within a pond]

Model ka -2LogL AICc

b ∆AICcc wd Evidence 

ratioe

Year + DistBay + DistBay2 + DistLevee 14 -22.2 20.3 0.00 0.31 1.00

Year + DistBay + DistBay2 + DistLevee + 

IslandArea

15 -25.3 21.9 1.63 0.14 2.25

Year + DistBay + DistBay2 + DistLevee + 

DistLevee2

15 -23.7 23.4 3.15 0.06 4.84

Year + DistBay + DistBay2 + IslandShape + 

IslandShape2

15 -23.7 23.5 3.18 0.06 4.90

Year + DistBay + IslandShape + IslandShape2 14 -18.4 24.1 3.78 0.05 6.62

Year + DistBay 12 -9.2 24.8 4.56 0.03 9.80

Year + DistBay + DistBay2 + DistLevee + 

IslandShape

15 -22.2 24.9 4.64 0.03 10.17

Year 11 -5.3 25.0 4.71 0.03 10.55

Year + DistBay + DistBay2 + IslandShape + 

IslandShape2 + DistLevee

16 -26.6 25.5 5.26 0.02 13.84

Year + DistBay + DistLevee 13 -12.6 25.6 5.31 0.02 14.20

Year + DistBay + DistBay2 13 -12.3 25.8 5.56 0.02 16.09

Year + DistBay + DistBay2 + IslandArea 14 -16.6 25.9 5.66 0.02 16.94

Year + DistBay + DistBay2 + DistLevee + 

DistLevee2 + IslandArea

16 -26.0 26.1 5.86 0.02 18.70

Year + IslandShape + IslandShape2 13 -12.0 26.2 5.88 0.02 18.94

Year + DistBay + DistBay2 + IslandArea + 

DistLevee + IslandShape

16 -25.8 26.4 6.08 0.01 20.91

Year + DistLevee 12 -7.7 26.4 6.10 0.01 21.15

Year + DistBay + DistBay2 + IslandArea + 

IslandArea2 + DistLevee

16 -25.5 26.7 6.38 0.01 24.33

Year + DistBay + DistBay2 + IslandShape + 

IslandShape2 + IslandArea

16 -25.2 27.0 6.69 0.01 28.3/6

Year + IslandShape 12 -7.0 27.1 6.80 0.01 29.98

Year + DistBay + IslandArea 13 -10.4 27.7 7.44 0.01 41.32

a

The number of parameters in the model including the intercept and variance.

b

Akaike’s Information Criterion corrected for small sample size.

c

The difference in the AICc values of the current model and the model with the lowest AICc. d

Akaike model weight. The likelihood of the model given the data, relative to other models in the candidate set. e

The weight of evidence that the model with the lowest AICc value is better than the current model. 
63

Table 6. Microhabitat characteristics examined in comparing American avocet and Forster’s tern nest sites to 

paired random sites in South San Francisco Bay, California, 2011 and 2012.

[Abbreviations: m2

, square meter; cm, centimeter; m, meter]

Microhabitat characteristic Abbreviation Description

Vegetation presence/absence VegYN Yes or No variable indicating whether any vegetation 

was present within the 1-m2 area

Percent vegetation cover %Veg Percent of the 1-m2 area covered by vegetation

Percent water cover %Water Percent of the 1-m2 area covered by water

Average vegetation height VegHt Mean vegetation height (cm) of all the vegetation in the 

1-m2 area

Slope Slope Maximum slope across the 1-m2 area

Aspect Aspect Downslope direction of the maximum slope

Surface ruggedness Ruggedness Vector Ruggedness Measure (VRM) of the terrain 

within the 1-m2 area

Distance to water DistWater Distance to nearest water

Elevation Elevation Elevation above the water surface of the pond

Distance to nearest conspecific 

nest

NearestConspecificNest Distance (m) to the nearest active conspecific nest

Distance to nearest nest NearestNest Distance (m) to the nearest active avocet or tern nest
64

Table 7. Number of American avocet nests and Forster’s tern nests with precise spatial data using real-time kinematic GPS, South San Francisco 

Bay, California, 2011 and 2012. 

[Only islands and the nests therein where the island area is provided were used in the island grid scale analysis. The number of nests in the island grid scale 

includes all nests initiated on the 24 islands for which we also had island topographic data. The number of nests in the microhabitat scale includes only those 

nests for which we recorded microhabitat surface topography in addition to the nest bowl location. Abbreviations: m2

, square meter; -, denote islands where 

birds nested but nest spatial data not recorded; 0, indicate that birds did not nest on the island] 

Pond Island Island area 

(m2) 

Island grid scale Microhabitat scale

Number of avocet 

nests

Number of tern 

nests

Number of avocet 

nests

Number of tern 

nests

2011 2012 2011 2012 2011 2012 2011 2012

A1 South 

Island

98(48)1 14 5 93 44 6 0 30 0 

A2W North 

Island

95 2 0 26 49 2 0 21 30

A2W Middle 

Island

138 16 8 127 159 7 0 27 40

A2W South 

Island

321 29 15 123 121 17 9 29 80

AB1 Northwest 

Island

377 7 - 8 - 4 - 7 -

AB2 Western 

Island 1

- - - - - - 4 - 4

AB2 Surprise 

Island 8

196 0 46 0 33 0 28 0 20

A7 Lone 

Island

28 1 0 13 0 0 0 9 0

A7 Horseshoe 

Island 1

42 7 1 35 30 2 0 22 16

A7 Horseshoe 

Island 2

44 8 2 37 39 5 0 24 7

A7 Horseshoe 

Island 3

39 2 1 36 41 1 1 29 34

A16 Island 1 536 5 0 0 0 3 0 0 0

A16 Island 2 575 28 0 8 0 24 0 4 0

A17 Northeast 

Island

- - - - - 0 22 0 0
65

Pond Island Island area 

(m2) 

Island grid scale Microhabitat scale

Number of avocet 

nests

Number of tern 

nests

Number of avocet 

nests

Number of tern 

nests

2011 2012 2011 2012 2011 2012 2011 2012

R1 Southwest 

Island 1

1,450 13 1 0 0 10 0 0 0 

R1 Southwest 

Island 2

2,451 33 3 0 0 26 1 0 0

SF2 Island 9 1,446 7 0 0 0 7 0 0 0

SF2 Island 11 1,423 7 0 0 0 5 0 0 0

SF2 Island 13 2,038 6 0 0 0 4 0 0 0

SF2 Island 14 1,998 9 0 0 0 7 0 0 0

SF2 Island 23 - - - - - 1 0 0 0

SF2 Island 24 1,440 11 0 0 0 9 0 0 0

SF2 Island 25 2,054 11 -

2 0 0 4 1 0 0

SF2 Island 27 1,544 9 0 0 0 7 0 0 0

SF2 Island 29 - - - - - 0 1 0 0

SF2 Island 30 1,463 18 -

2 0 0 9 1 0 0

E2 Island 1 805 6 - 0 0 0 - 0 0

E2 Island 2 1,232 14 - 0 0 1 - 0 0

Total 263 82 506 516 161 68 202 231

1

Higher water level in 2012 resulted in reduction of island area to 48m2. 2

One nest was found but was not included in the analysis.
66

Table 8. Model selection results for logistic regression mixed models for the probability that an island grid cell was used for nesting by American 

avocets and Forster’s terns, South San Francisco Bay, California, 2011 and 2012. 

[All models include nesting island as a random effect] 

Model ka -2LogL AICcb ∆AICc

c wd Evidence 

ratioe

American avocets

Year + Elevation + Elevation2 + DistWater + DistWater2 + 

NumberBorderingConspecificNests + NumberBorderingConspecificNests2 + Slope

10 2888.3 2908.3 0.0 0.30 1.00

Year + Elevation + Elevation2 + DistWater + DistWater2 + 

NumberBorderingConspecificNests + NumberBorderingConspecificNests2 + Slope + 

Slope2

11 2887.8 2909.8 1.5 0.14 2.14

Year + Elevation + Elevation2 + DistWater + DistWater2 + 

NumberBorderingConspecificCells + NumberBorderingConspecificCells2 + Slope

10 2890.2 2910.2 1.9 0.12 2.58

Year + Elevation + Elevation2 + DistWater + DistWater2 + 

NumberBorderingConspecificNests + NumberBorderingConspecificNests2 + Slope + 

Aspect

11 2888.2 2910.2 2.0 0.11 2.65

Year + Elevation + Elevation2 + DistWater + DistWater2 + 

NumberBorderingConspecificNests + NumberBorderingConspecificNests2 + Slope + 

Aspect + Aspect2

12 2887.2 2911.2 2.9 0.07 4.27

Year + Elevation + Elevation2 + DistWater + DistWater2 + 

NumberBorderingConspecificCells + NumberBorderingConspecificCells2 + Slope + 

Slope2

11 2889.7 2911.7 3.4 0.05 5.56

Year + Elevation + Elevation2 + DistWater + DistWater2 + 

NumberBorderingConspecificNests + NumberBorderingConspecificNests2 + Slope + 

Slope2 + Aspect

12 2887.8 2911.8 3.5 0.05 5.70

Year + Elevation + Elevation2 + DistWater + DistWater2 + 

NumberBorderingConspecificCells + NumberBorderingConspecificCells2 + Slope + 

Aspect

11 2890.2 2912.2 3.9 0.04 6.93

Forster’s terns

Elevation + Elevation2 + DistWater + DistWater2 + NumberBorderingConspecificCells 

+ NumberBorderingConspecificCells2

8 2220.0 2236.0 0.0 0.09 1.00

Elevation + DistWater + DistWater2 + NumberBorderingConspecificCells + 

NumberBorderingConspecificCells2

7 2222.1 2236.1 0.1 0.09 1.04

Elevation + Elevation2 + DistWater + DistWater2 + NumberBorderingConspecificCells 

+ NumberBorderingConspecificCells2 + Aspect

9 2219.0 2237.0 1.0 0.06 1.67

Elevation + DistWater + DistWater2 + NumberBorderingConspecificCells + 

NumberBorderingConspecificCells2 + Aspect

8 2221.3 2237.3 1.3 0.05 1.88

Elevation + DistWater + DistWater2 + NumberBorderingConspecificCells + 8 2221.4 2237.5 1.5 0.05 2.07
67

Model ka -2LogL AICcb ∆AICc

c wd Evidence 

ratioe

NumberBorderingConspecificCells2 + Year

Elevation + Elevation2 + DistWater + DistWater2 + NumberBorderingConspecificCells 

+ NumberBorderingConspecificCells2 + Year

9 2219.5 2237.5 1.5 0.04 2.10

Elevation + DistWater + DistWater2 + NumberBorderingConspecificCells + 

NumberBorderingConspecificCells2 + Slope + Slope2

9 2219.6 2237.6 1.6 0.04 2.21

Elevation + Elevation2 + DistWater + DistWater2 + NumberBorderingConspecificCells 

+ NumberBorderingConspecificCells2 + Aspect + Aspect2

10 2217.6 2237.7 1.7 0.04 2.28

Elevation + DistWater + DistWater2 + NumberBorderingConspecificCells + 

NumberBorderingConspecificCells2 + Aspect + Aspect2

9 2219.8 2237.9 1.9 0.04 2.53

Elevation + Elevation2 + DistWater + DistWater2 + NumberBorderingConspecificCells 

+ NumberBorderingConspecificCells2 + Slope

9 2220.0 2238.0 2.0 0.03 2.72

a

The number of parameters in the model including the intercept and variance.

b

Akaike’s Information Criterion corrected for small sample size.

c

The difference in the AICc values of the current model and the model with the lowest AICc. d

Akaike model weight. The likelihood of the model given the data, relative to other models in the candidate set. e

The weight of evidence that the model with the lowest AICc value is better than the current model.
68

Table 9. Model selection results for conditional logistic regression models evaluating differences between nest 

microhabitat and the microhabitat of a random paired site among American avocet and Forster’s tern nests, South 

San Francisco Bay, California, 2011 and 2012.

Model ka -2LogL AICcb ∆AICcc wd Evidence 

ratioe

American avocet

VegYN + NearestConspecificNest + Aspect 3 246.1 252.2 0.0 0.16 1.00

VegYN + NearestConspecificNest + Aspect + 

Ruggedness

4 245.7 253.8 1.6 0.07 2.26

VegYN + NearestConspecificNest + Aspect + 

DistWater

4 245.8 253.9 1.8 0.06 2.41

VegYN + NearestConspecificNest + Aspect + 

Slope

4 246.1 254.1 2.0 0.06 2.69

VegYN + NearestConspecificNest + Aspect + 

Elevation

4 246.1 254.2 2.0 0.06 2.76

VegYN + NearestConspecificNest 2 250.3 254.3 2.1 0.05 2.87

VegYN + NearestConspecificNest + Aspect + 

Ruggedness + Slope

5 245.1 255.2 3.0 0.03 4.51

VegYN + NearestConspecificNest + Slope 3 249.3 255.3 3.2 0.03 4.84

VegYN + NearestConspecificNest + Aspect + 

Ruggedness + DistWater

5 245.5 255.6 3.4 0.03 5.52

VegYN + NearestConspecificNest + 

DistWater

3 249.8 255.8 3.6 0.03 6.17

VegYN + NearestConspecificNest + Aspect + 

Ruggedness + Elevation

5 245.7 255.8 3.7 0.02 6.27

VegYN + NearestNest + Aspect 3 249.8 255.8 3.7 0.02 6.27

VegYN + NearestConspecificNest + Aspect + 

DistWater + Slope

5 245.8 255.9 3.8 0.02 6.51

VegYN + NearestConspecificNest + Aspect + 

DistWater + Elevation

5 245.8 256.0 3.8 0.02 6.63

VegYN + NearestConspecificNest + Aspect + 

Slope + Elevation

5 246.1 256.2 4.0 0.02 7.46

Forster’s tern

%Veg + Elevation + NearestConspecificNest 

+ Aspect

4 546.9 554.9 0.0 0.20 1.00

%Veg + Elevation + NearestConspecificNest 

+ Aspect + Slope

5 545.9 556.0 1.1 0.12 1.69

%Veg + Elevation + NearestConspecificNest 

+ Aspect + Slope + Ruggedness

6 544.4 556.5 1.6 0.09 2.21

%Veg + Elevation + NearestConspecificNest 

+ Aspect + Ruggedness

5 546.6 556.7 1.8 0.08 2.41

%Veg + Elevation + NearestConspecificNest 

+ Aspect + DistWater

5 546.8 556.9 2.0 0.07 2.70

%Veg + Elevation + NearestConspecificNest 

+ Aspect + Slope + DistWater

6 545.8 557.9 3.0 0.04 4.56

%Veg + Elevation + NearestConspecificNest 3 552.4 558.5 3.6 0.03 5.94

%Veg + Elevation + NearestConspecificNest 

+ Aspect + Slope + Ruggedness + DistWater

7 544.3 558.5 3.6 0.03 5.95

%Veg + Elevation + NearestConspecificNest 

+ Aspect + Ruggedness + DistWater

6 546.5 558.6 3.7 0.03 6.36
69

Model ka -2LogL AICc

b ∆AICcc wd Evidence 

ratioe

%Veg + Elevation + NearestConspecificNest 

+ Slope

4 550.8 558.8 3.9 0.03 7.04

VegYN + Elevation + NearestConspecificNest 

+ Aspect

4 551.0 559.0 4.1 0.03 7.81

a

The number of parameters in the model including the intercept and variance.

b

Akaike’s Information Criterion corrected for small sample size.

c

The difference in the AICc values of the current model and the model with the lowest AICc. d

Akaike model weight. The likelihood of the model given the data, relative to other models in the candidate set.

e

The weight of evidence that the model with the lowest AICc value is better than the current model.
70

Table 10. Model averaged parameter estimates, standard errors (SE), lower (LCL) and upper (UCL) 95 percent

confidence limits, and standardized odds ratios for variables examined for differences between American avocet 

nest microhabitat and random paired site microhabitat and Forster’s tern nest microhabitat and random paired site 

microhabitat, South San Francisco Bay, California, 2011 and 2012. 

[Bolded entries indicate variables with model-averaged parameter estimates in which the 95 percent confidence limits did not 

overlap zero] 

Species Variable Estimate SE LCL UCL Odds ratio

American 

avocet

Elevation 0.031 0.860 -1.654 1.717 1.015

DistWater 0.040 0.070 -0.098 0.178 1.177

NearestConspecificNest -0.104 0.041 -0.184 -0.024 5.861

NearestNest -0.107 0.043 -0.191 -0.023 5.737

Slope -0.014 0.022 -0.057 0.030 1.096

Ruggedness 17.397 25.598 -32.776 67.570 1.028

%Veg 1.755 0.561 0.656 2.854 2.127

VegHt 0.059 0.026 0.007 0.110 1.509

%Water -3.046 2.025 -7.014 0.922 1.000

Aspect -0.350 0.181 -0.704 0.004 1.602

VegYN=Y 1.419 0.274 0.882 1.957 4.135

Forster’s 

tern

Elevation 2.549 0.758 1.063 4.034 2.469

DistWater -0.003 0.151 -0.299 0.293 1.003

NearestConspecificNest -0.154 0.068 -0.286 -0.021 1.269

NearestNest -0.054 0.063 -0.178 0.071 1.059

Slope -0.013 0.011 -0.035 0.008 1.153

Ruggedness 7.375 9.476 -11.198 25.948 1.041

%Veg 1.272 0.339 0.607 1.937 2.597

VegHt 0.009 0.015 -0.020 0.038 1.082

%Water -1.733 1.045 -3.782 0.316 1.053

Aspect -0.247 0.113 -0.468 -0.026 1.428

VegYN=Y 0.443 0.137 0.173 0.712 1.557
71

Table 11. Total abundance, relative percentage, and frequency of all birds observed in the units of Pond SF2, 

South San Francisco Bay, California, October through May 2010–12.

Common name Scientific name Guild

Abundance Relative percentage Frequency (percent)

Y1 Y2 Comb

ined Y1 Y2 Combined Y1 Y2 Combined

Snowy Egret Egretta thula Heron 1189 1178 2367 1.24 0.71 0.90 100 100 100

Double-crested 

Cormorant

Phalacrocorax 

auritus

Piscivore 1096 2068 3164 1.14 1.24 1.20 96.67 100 98.21

Great Egret Ardea alba Heron 181 726 907 0.19 0.44 0.34 96.67 100 98.21

Least Sandpiper Calidris minutilla Sm. 

Shorebird

3784 4969 8753 3.94 2.98 3.33 96.67 92.31 94.64

Mallard Anas platyrhynchos Dabbler 409 1126 1535 0.43 0.67 0.58 93.33 96.15 94.64

Northern Shoveler Anas clypeata Dabbler 13056 5852 18908 13.59 3.51 7.19 93.33 92.31 92.86

American Wigeon Anas americana Dabbler 2542 2780 5322 2.65 1.67 2.02 86.67 96.15 91.07

Ruddy Duck Oxyura jamaicensis Diver 4569 9899 14468 4.76 5.93 5.50 83.33 92.31 87.50

Dunlin Calidris alpina Sm. 

Shorebird

4210 13115 17325 4.38 7.86 6.59 93.33 76.92 85.71

Western Sandpiper Calidris mauri Sm. 

Shorebird

43156 45941 89097 44.92 27.53 33.89 93.33 73.08 83.93

Willet Catoptrophorus 

semipalmatus X

Med. 

Shorebird

4105 8225 12330 4.27 4.93 4.69 70.00 96.15 82.14

Ring-billed Gull Larus delawarensis Gull 496 1190 1686 0.52 0.71 0.64 66.67 100 82.14

American Coot Fulica americana Dabbler 808 1147 1955 0.84 0.69 0.74 73.33 84.62 78.57

Greater Yellowlegs Tringa melanoleuca Med. 

Shorebird

59 142 201 0.06 0.09 0.08 60.00 100.00 78.57

Northern Pintail Anas acuta Dabbler 1444 9516 10960 1.50 5.70 4.17 60.00 92.31 75.00

Bufflehead Bucephala albeola Diver 1805 1712 3517 1.88 1.03 1.34 70.00 80.77 75.00

California Gull Larus californicus Gull 107 1657 1764 0.11 0.99 0.67 60.00 92.31 75.00

Gadwall Anas strepera Dabbler 135 793 928 0.14 0.48 0.35 56.67 92.31 73.21

Great Blue Heron Heron Ardea Heron 39 121 160 0.04 0.07 0.06 60.00 88.46 73.21

Marbled Godwit Limosa fedoa Med. 

Shorebird

1500 17125 18625 1.56 10.26 7.08 53.33 92.31 71.43

Dowitcher (Longbilled and Short-billed)Limnodromus spp Sm. 

Shorebird

2168 7312 9480 2.26 4.38 3.61 50.00 92.31 69.64

Herring Gull Larus argentatus Gull 43 251 294 0.04 0.15 0.11 53.33 88.46 69.64

American Avocet Recurvirostra 

americana

Med. 

Shorebird

4639 13935 18574 4.83 8.35 7.06 63.33 73.08 67.86

Long-billed Curlew Numenius 

americanus

Med. 

Shorebird

564 1254 1818 0.59 0.75 0.69 43.33 92.31 66.07

Common Goldeneye Bucephala clangula Diver 336 174 510 0.35 0.10 0.19 70.00 53.85 62.50

Forster's Tern Sterna forsteri Tern 543 474 1017 0.57 0.28 0.39 60.00 61.54 60.71

Eared Grebe Podiceps nigricollis Eared 

Grebe

102 36 138 0.11 0.02 0.05 66.67 53.85 60.71

Black-necked Stilt Himantopus 

mexicanus

Med. 

Shorebird

185 179 364 0.19 0.11 0.14 43.33 73.08 57.14

Black-bellied Plover Pluvialis squatarola Med. 

Shorebird

201 1016 1217 0.21 0.61 0.46 36.67 76.92 55.36

Canvasback Aythya valisineria Diver 83 3486 3569 0.09 2.09 1.36 50.00 57.69 53.57

Western Gull Larus occidentalis Gull 30 136 166 0.03 0.08 0.06 30.00 76.92 51.79

Semipalmated Plover Charadrius 

semipalmatus

Sm. 

Shorebird

1809 1350 3159 1.88 0.81 1.20 40.00 57.69 48.21

Scaup (Greater and 

Lesser)

Aythya spp Diver 179 1099 1278 0.19 0.66 0.49 43.33 53.85 48.21

Whimbrel Numenius phaeopus Med. 

Shorebird

126 74 200 0.13 0.04 0.08 30.00 69.23 48.21
72

Common name Scientific name Guild

Abundance Relative percentage Frequency (percent)

Y1 Y2 Comb

ined Y1 Y2 Combined Y1 Y2 Combined

Red-breasted 

Merganser

Mergus serrator Piscivore 5 128 133 <0.01 0.08 0.05 13.33 73.08 41.07

American Greenwinged TealAnas crecca Dabbler 20 5725 5745 0.02 3.43 2.18 13.33 69.23 39.29

Canada Goose Branta canadensis Goose 86 90 176 0.09 0.05 0.07 36.67 38.46 37.50

Horned Grebe Podiceps auritus Eared 

Grebe

30 15 45 0.03 0.01 0.02 40.00 26.92 33.93

Pied-billed Grebe Podilymbus podiceps Piscivore 3 38 41 <0.01 0.02 0.02 10.00 61.54 33.93

Brown Pelican Pelecanus 

occidentalis

Piscivore 56 135 191 0.06 0.08 0.07 33.33 30.77 32.14

Lesser Yellowlegs Tringa flavipes Med. 

Shorebird

15 21 36 0.02 0.01 0.01 26.67 38.46 32.14

Killdeer Charadrius vociferus Med. 

Shorebird

18 9 27 0.02 0.01 0.01 26.67 23.08 25.00

American White 

Pelican

Pelican pelecanus Piscivore 14 537 551 0.01 0.32 0.21 10.00 30.77 19.64

Western Grebe Aechmophorus 

occidentalis

Piscivore 4 17 21 <0.01 0.01 0.01 6.67 30.77 17.86

Western Snowy Plover Charadrius 

alexandrinus

Sm. 

Shorebird

64 1 65 0.07 <0.01 0.02 26.67 3.85 16.07

Peregrine Falcon Falco peregrinus Raptor 9 3 12 <0.01 <0.01 <0.01 16.67 7.69 12.50

Caspian Tern Sterna caspia Tern 0 32 32 0.00 0.02 0.01 0 23.08 10.71

Eurasian Wigeon Anas penelope Dabbler 3 3 6 <0.01 <0.01 <0.01 10.00 11.54 10.71

Common Merganser Mergus merganser Piscivore 0 21 21 0.00 0.01 0.01 0 19.23 8.93

Common Raven Corvus corax Passerine 5 5 10 <0.01 <0.01 <0.01 10.00 7.69 8.93

Elegant Tern Sterna elegans Tern 0 7 7 0.00 <0.01 <0.01 0 19.23 8.93

Northern Harrier Circus cyaneus Raptor 6 1 7 <0.01 <0.01 <0.01 13.33 3.85 8.93

Bonaparte's Gull Larus philadelphia Gull 27 0 27 0.03 <0.01 0.01 10.00 0 5.36

Black-crowned NightHeronNycticorax nycticorax Heron 2 1 3 <0.01 <0.01 <0.01 6.67 3.85 5.36

Spotted Sandpiper Actitis macularia Sm. 

Shorebird

4 0 4 <0.01 <0.01 <0.01 6.67 0 3.57

Clark's Grebe Aechmophorus clarkii Piscivore 0 2 2 <0.01 <0.01 <0.01 0 7.69 3.57

Merlin Falco columbarius Raptor 2 0 2 <0.01 <0.01 <0.01 3.33 0 1.79

Glaucous-winged Gull Larus glaucescens Gull 2 0 2 <0.01 <0.01 <0.01 3.33 0 1.79

Barrow's Goldeneye Bucephala islandica Diver 0 2 2 <0.01 <0.01 <0.01 0 3.85 1.79

Red Knot Calidris canutus Med. 

Shorebird

2 0 2 <0.01 <0.01 <0.01 3.33 0 1.79

Red-tailed Hawk Buteo jamaicensis Raptor 1 0 1 <0.01 <0.01 <0.01 3.33 0 1.79

Thayer's Gull Larus thayeri Gull 1 0 1 <0.01 <0.01 <0.01 3.33 0 1.79

White-tailed Kite Elanus leucurus Raptor 0 1 1 <0.01 <0.01 <0.01 0.00 3.85 1.79

Blue-winged Teal Anas discors Dabbler 1 0 1 <0.01 <0.01 <0.01 3.33 0 1.79

Mew Gull Larus canus Gull 1 0 1 <0.01 <0.01 <0.01 3.33 0 1.79

Ruddy Turnstone Arenaria interpres Med. 

Shorebird

0 1 1 <0.01 <0.01 <0.01 0 3.85 1.79
73

Table 12. Guild relative abundance variance among islands in Pond SF2 (MANOVA, df =29), South San Francisco 

Bay, California. 

[Bold indicates a significant p-value. Abbreviations: -, indicate that there was not enough data to complete the test] 

Tide Guild F p-value

High Dabbler 4.34 <0.001

Diver 0.91 0.608

Gull 1.72 0.011

Heron 1.25 0.171

Medium 

Shorebird 4.24 <0.001

Piscivore 1.73 0.010

Raptor 0.95 0.549

Small Shorebird 2.86 <0.001

Tern 0.72 0.867

Low Dabbler 3.41 <0.001

Diver 1.32 0.117

Gull 1.53 0.037

Heron 1.46 0.054

Medium 

Shorebird 1.68 0.013

Piscivore 1.41 0.075

Raptor 1.00 0.466

Small Shorebird 1.06 0.384

Tern - -
74

Table 13. The presence or absence of guilds by island shape and size in Pond SF2 (binary logistic regression, df 

=1), South San Francisco Bay, California. 

[Bold indicates a significant p-value. 95-percent confidence intervals (95% C.I.) were only calculated when results were 

significant. -, not enough data to complete the test] 

Guild

Low tide High tide

Island shape Island size Island shape Island size

Odds 

ratio 95% C.I. pvalue

Odds 

ratio 95% C.I. pvalue

Odds 

ratio 95% C.I. pvalue

Odds 

ratio 95% C.I. pvalue

Dabbler 0.744 - 0.235 0.999 0.998-1.000 0.005 0.827 - 0.417 0.999 0.998-1.000 0.030

Diver 1.905 - 0.542 1.000 - 0.774 0.638 - 0.606 0.998 - 0.159

Gull 2.708 - 0.058 1.002 1.000-1.003 0.029 3.222 1.325-7.839 0.010 1.002 1.001-1.003 0.007

Heron 0.931 - 0.878 0.999 - 0.394 2.682 1.153-6.240 0.022 1.001 - 0.181

Medium 

Shorebird 0.577 - 0.264 1.000 - 0.879 1.113 - 0.781 1.000 - 0.654

Piscivore 11.484

1.020-

129.331 0.048 1.003 - 0.139 3.119 - 0.095 1.002 - 0.149

Raptor 0.000 - 0.958 0.678 - 0.932 0.209 - 0.426 0.998 - 0.598

Small 

Shorebird 1.084 - 0.903 1.001 - 0.199 0.484 0.279-0.840 0.010 1.000 - 0.661

Tern - - - - - - 1.415 - 0.766 1.002 - 0.340
75

Table 14. Number and area of islands in 22 ponds used to model foraging and roosting bird abundance using 

historic bird data collected between 2002–13, South San Francisco Bay, California.

[Pond: Ponds starting with A are in the Alviso complex, ponds starting with E are in the Eden Landing complex, and ponds 

starting with R are in the Ravenswood complex. Area ratio: The area of all islands within a pond divided by the pond area.] 

Pond # Island Total

island area (ha) Area ratio

A1 2 0.131 0.001

A13 2 0.171 0.002

A16 5 0.343 0.003

A17 1 0.150 0.003

A2W 3 0.077 0.000

A5 1 0.052 0.000

A7 7 0.044 0.000

AB1 1 0.042 0.001

AB2 17 0.674 0.009

E1 1 0.058 0.000

E10 3 0.386 0.004

E2 6 0.887 0.003

E2C 1 0.002 0.000

E3C 1 0.435 0.006

E4 2 0.028 0.000

E6 1 0.058 0.001

E6A 2 0.276 0.002

E7 2 0.216 0.002

E9 2 0.054 0.000

R1 3 0.684 0.004

RSF2U1 8 1.402 0.061

RSF2U2 22 4.026 0.119
76

Table 15. Common name and associated foraging guild of waterbird species observed in pondsSouth San 

Francisco Bay, California, October through April 2002–13.

[Twenty incidental species, for which we observed ten or fewer individuals across the study period, were excluded from this 

list. Species that are evaluated individually in modeling efforts are identified by four-letter species coding.]

Species common name Foraging guild

American Green-winged Teal Dabbling duck

American Avocet (AMAV) Medium shorebird

American Coot Dabbling duck

American Wigeon Dabbling duck

American White Pelican Piscivore

Black-bellied Plover Medium shorebird

Black-crowned Night-Heron Heron

Belted Kingfisher Piscivore

Black Skimmer Piscivore

Black Turnstone Medium shorebird

Black-necked Stilt (BNST) Medium shorebird

Bonaparte's Gull Gull

Brown Pelican Piscivore

Bufflehead Diving duck

Blue-winged Teal Dabbling duck

California Gull Gull

Canvasback Diving duck

Caspian Tern Piscivore

Cinnamon Teal Dabbling duck

Clark's Grebe Piscivore

Common Goldeneye Diving duck

Common Merganser Piscivore

Double-crested Cormorant Piscivore

Short or Long-billed Dowitcher Small shorebird

Dunlin Small shorebird

Eared Grebe (EAGR) Eared grebe

Elegant Tern Piscivore

Eurasian Wigeon Dabbling duck

Forster's Tern (FOTE) Piscivore

Gadwall Dabbling duck

Great Blue Heron Heron

Great Egret Heron

Greater Yellowlegs Medium shorebird

Glaucous-winged Gull Gull

Herring Gull Gull

Horned Grebe Eared grebe

Hooded Merganser Piscivore

Killdeer Medium shorebird

Long-billed Curlew Medium shorebird

Least Sandpiper Small shorebird

Least Tern Piscivore

Lesser Yellowlegs Medium shorebird

Marbled Godwit Medium shorebird

Mallard Dabbling duck

Mew Gull Gull

Northern Pintail Dabbling duck

Northern Shoveler Dabbling duck

Pied-billed Grebe Piscivore

Ring-billed Gull Gull

Red-breasted Merganser Piscivore

Redhead Diving duck

Red Knot Medium shorebird

Red-throated Loon Piscivore

Ruddy Duck Diving duck

Ruddy Turnstone Medium shorebird

Sanderling Small shorebird

Greater or Lesser Scaup Diving duck

Semipalmated Plover Small shorebird

Snowy Egret Heron

Snowy Plover Small shorebird

Surf Scoter Diving duck

Thayer's Gull Gull

Western Grebe Piscivore

Western Gull Gull

Western Sandpiper (WESA) Small shorebird

Whimbrel Medium shorebird

Willet Medium shorebird

White-winged scoter Diving duck
77

Table 16. Top ranking models at the pond scale for the abundance of foraging and roosting avian guilds and 

species in the former salt production ponds, South San Francisco Bay, Californiaa. 

Species or 

guild Model structure Kb -2LogL AICCc wi

d

Foraging

AMAV Base + #Island 17 9729.00 9763.26 0.36

BNST Base 16 6974.36 7006.59 0.23

EAGR Base + HasIsland + Month × HasIsland 23 11387.40 11433.87 1.00

FOTE Base + #Island + IslandArea + #Island × IslandArea + 

#Island x Month + IslandArea × Month + #Island × 

IslandArea × Month

37 4268.02 4343.23 0.85

WESA Base + HasIsland + Month × HasIsland 23 14484.90 14531.37 0.76

Dabbler Base + HasIsland 17 20568.20 20602.46 0.25

Diver Base + HasIsland + Month × HasIsland 23 18274.08 18320.55 0.17

Piscivores Base + #Island + IslandArea + IslandArea × Month 24 11476.08 11524.59 0.28

Gull Base + #Island + IslandArea + IslandArea × Month 24 8858.84 8907.35 0.61

Heron Base + #Island + AreaRatio 18 9071.62 9107.91 0.39

MedShore Base + HasIsland + Month × HasIsland 23 15599.60 15646.07 0.68

SmShore Base + HasIsland + Month × HasIsland 23 21311.40 21357.87 1.00

Roosting

AMAV Base + #Island + AreaRatio + AreaRatio × Month 24 12941.20 12989.71 0.28

BNST Base + HasIsland 17 5106.44 5140.70 0.18

EAGR Base 16 9965.36 9997.59 0.20

FOTE Base + #Island + AreaRatio + #Island × AreaRatio + 

#Island × Month

25 5492.40 5542.96 0.34

WESA Base + HasIsland + Month × HasIsland 23 13404.56 13451.03 0.90

Dabbler Base + #Island + AreaRatio + #Island × AreaRatio 19 22903.20 22941.52 0.25

Diver Base + HasIsland + Month × HasIsland 23 24068.40 24114.87 0.98

Piscivores Base + #Island + AreaRatio 18 14693.06 14729.35 0.19

Gull Base + #Island + AreaRatio + #Island × AreaRatio + 

#Island × Month

25 20258.60 20309.16 0.87

Heron Base + #Island + AreaRatio + #Island × AreaRatio 19 9420.86 9459.18 0.58

MedShore Base + #Island + AreaRatio + #Island × AreaRatio 19 20700.60 20738.92 0.69

SmShore Base + HasIsland 17 21016.00 21050.26 0.77

a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio, 

and interactions between these variables and between these variable and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s Information Criterion. d

Weight of the model relative to candidate models. Full candidate model sets are reported in appendix 1. 
78

Table 17. Parameter estimatesa of island variables from the top-ranked models of foraging bird abundance at the pond scaleb, South San Francisco 

Bay, California. 

Island

Variablec

American 

Avocets

Eared 

Grebes

Forster's 

Terns

Western 

Sandpipers

Dabbling 

Ducks

Small 

Shorebirds

Medium 

Shorebirds

Piscivore

s

Gulls

HasIsland 2.20

HasIsland (Oct) 1.49 3.40 2.29 1.30

HasIsland (Nov) 1.34 3.02 1.91 1.23

HasIsland (Dec) 1.00 1.86 1.60 1.53

HasIsland (Jan) 0.63 3.41 2.80 0.68

HasIsland (Feb) -0.10 1.63 1.23 0.35

HasIsland (Mar) 0.11 1.92 1.40 0.96

HasIsland (Apr) -0.19 2.04 0.96 1.73

Number Islands 0.16

# Islands (Oct) -0.15

# Islands (Nov) 0.32

# Islands (Dec) -0.23

# Islands (Jan) 0.43

# Islands (Feb) 0.04

# Islands (Mar) 0.40

# Islands (Apr) 0.47

Island Area

Island Area (Oct) 115.81 75.66 85.11

Island Area (Nov) 773.75 21.70 615.57

Island Area (Dec) -52.41 -19.44 136.48

Island Area (Jan) 342.87 5.92 102.20

Island Area (Feb) -559.58 -27.66 125.04

Island Area (Mar) 510.32 11.98 106.79

Island Area (Apr) -52.89 67.80 141.76

# Islands x Area

# Islands x Area (Oct) 2.39

# Islands x Area (Nov) -152.66

# Islands x Area (Dec) 7.31

# Islands x Area (Jan) -91.71

# Islands x Area (Feb) 19.01

# Islands x Area (Mar) -105.73

# Islands x Area (Apr) -49.16 a

Positive and negative parameter estimate values reflect the direction of the relationship between avian abundance and the variable of interest.

b

Species and guilds lacking significant parameter estimates in their top models are excluded from the table. c

Island variables followed by month refer to the interaction between the variable and the particular month.
79

Table 18. Parameter estimatesa of island variables from the top-ranked models of roosting bird abundance at the 

pond scaleb, South San Francisco Bay, California. 

Island

variablec

Forster's 

terns

Western 

sandpipers

Dabbling 

ducks

Diving 

ducks

Small 

shorebirds

Medium 

shorebirds Gulls Herons

HasIsland 1.31

HasIsland (Oct) 0.53 0.95

HasIsland (Nov) 1.72 0.78

HasIsland (Dec) 0.34 1.02

HasIsland (Jan) 3.25 1.46

HasIsland (Feb) 1.02 0.60

HasIsland (Mar) 1.57 0.12

HasIsland (Apr) 1.23 0.10

Number Islands 0.25

# Islands (Oct) 0.14

# Islands (Nov) -0.26

# Islands (Dec) -0.07

# Islands (Jan) -0.05

# Islands (Feb) 0.07

# Islands (Mar) 0.12

# Islands (Apr) 0.18

Area Ratio 119.18 84.04 71.03 107.32 54.42

# Islands x Area Ratio -6.10 -3.40 -2.99 -3.77 -1.86 a

Positive and negative parameter estimate values reflect the direction of the relationship between avian abundance and the 

variable of interest.

b

Species and guilds lacking significant parameter estimates in their top models are excluded from the table. c

Island variables followed by month refer to the interaction between the variable and the particular month.
80

Table 19. The influence of island variables on the foraging abundance of birds in ponds, South San Francisco Bay, 

California. 

Species or Guild Presence of 

islandsa

Number of 

islands

Island 

area

Island-area to 

pond-area ratio

Temporal 

variationb

American avocets + + 0 0 0

Black-necked stilts 0 0 0 0 0

Eared grebes + 0 0 0 +

Forster's terns + - + 0 +

Western sandpipers + 0 0 0 +

Dabbling ducks + 0 0 0 0

Diving ducks 0 0 0 0 0

Gulls + 0 + 0 +

Herons 0 0 0 0 0

Medium shorebirds + 0 0 0 +

Piscivores + 0 + 0 +

Small shorebirds + 0 0 0 + a

Positive values were included for all birds with any positive island variable influence. b

Positive value indicates that a temporal variation exists and does not refer to the direction of the island effect. The direction 

of influence is reported in table 17.
81

Table 20. The influence of island variables on the roosting abundance of birds in ponds, South San Francisco Bay, 

California. 

Species or guild Presence of 

islandsa

Number of 

islands

Island 

area

Island-area to 

pond-area ratio

Temporal 

variationb

American avocets 0 0 0 0 0

Black-necked stilts 0 0 0 0 0

Eared grebes 0 0 0 0 0

Forster's terns + - 0 + +

Western sandpipers + 0 0 0 +

Dabbling ducks + - 0 + 0

Diving ducks + 0 0 0 +

Gulls + - 0 + 0

Herons + - 0 + 0

Medium shorebirds + - 0 + 0

Piscivores 0 0 0 0 0

Small shorebirds + 0 0 0 0 a

Positive values were included for all birds with any positive island variable influence. b

Positive value indicates that a temporal variation exists and does not refer to the direction of the island effect. The direction 

of influence is reported in table 18.
82

Table 21. Top ranking models at the grid scale for the abundance of foraging and roosting birds in the former salt 

production ponds, South San Francisco Bay, Californiaa. 

Species or 

guild Model structure kb -2LogL AICCc wi

d

Foraging

AMAV Base + IslandDist 19 10437.92 10476.02 0.65

BNST Base + #Island + AreaRatio + #Island × AreaRatio 21 5669.70 5711.83 0.31

EAGR Base + #Island + IslandArea 20 5129.94 5170.05 0.34

FOTE Base + #Island + IslandArea + #Island × IslandArea + 

#Island × Month

27 1893.75 1947.95 0.46

WESA Base + #Island + IslandArea + #Island × IslandArea + 

#Island × Month + IslandArea × Month + #Island × 

IslandArea × Month

39 18011.96 18090.39 0.70

Dabbler Base + HasIsland + Month × HasIsland 25 29246.60 29296.78 0.72

Diver Base + IslandDist + Month × IslandDist 25 16070.06 16120.24 1.00

Piscivores Base + #Island + AreaRatio + #Island × Month 26 9462.26 9514.45 0.23

Gull Base + IslandDist + Month × IslandDist 25 4924.12 4974.30 0.99

Heron Base + HasIsland + Month × HasIsland 25 9891.06 9941.24 0.39

MedShore Base + IslandDist + Month × IslandDist 25 18752.58 18802.76 0.39

SmShore Base + #Island + IslandArea + #Island × IslandArea + 

#Island × Month + IslandArea × Month

33 28951.00 29017.31 0.38

Roosting

AMAV Base + HasIsland 19 13234.04 13272.14 0.55

BNST Base + HasIsland + Month × HasIsland 25 3037.38 3087.56 0.63

EAGR Base + IslandDist + Month × IslandDist 25 3472.86 3523.04 0.68

FOTE Base + HasIsland + Month × HasIsland 25 3656.34 3706.52 0.42

WESA Base + HasIsland + Month × HasIsland 25 11488.60 11538.78 0.51

Dabbler Base + HasIsland + Month × HasIsland 25 35283.00 35333.18 0.98

Diver Base + #Island + IslandArea + #Island × IslandArea + 

#Island × Month + IslandArea × Month + #Island × 

IslandArea × Month

39 27964.60 28043.03 0.68

Piscivores Base + HasIsland 19 12551.28 12589.38 0.65

Gull Base + IslandDist + Month × IslandDist 25 16142.88 16193.06 0.99

Heron Base + HasIsland 19 7302.18 7340.28 0.53

MedShore Base + HasIsland 19 21922.40 21960.50 0.93

SmShore Base + HasIsland + Month × HasIsland 25 20649.20 20699.38 0.29 a

Birds were surveyed across the former salt ponds during October through April of 2002– 2013. Variables considered 

included the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pondarea ratio, distance from the grid to the nearest island (IslandDist), and interactions between these variables and between 

these variable and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion.

d

Weight of the model relative to candidate models. Full candidate model sets are reported in appendix 1. 
83

Table 22. Parameter estimatesa of island variables from the top-ranked models of foraging bird abundance at the grid scale.

Island

variableb

American 

avocets

Black-necked 

stilts

Eared 

grebes

Forster's 

terns

Western 

sandpipers

Dabbling 

ducks

Diving 

ducks

Small 

shorebirds

Medium 

shorebirds Piscivores Gulls Herons

HasIsland

HasIsland (Oct) 1.15 0.73

HasIsland (Nov) 0.27 1.15

HasIsland (Dec) 0.66 0.38

HasIsland (Jan) 0.80 0.01

HasIsland (Feb) 0.14 -0.25

HasIsland (Mar) -0.05 0.22

HasIsland (Apr) -0.19 0.50

Number Islands 0.83

# Islands (Oct) 1.18 0.92 0.64 0.11

# Islands (Nov) -0.51 1.04 0.31 -0.39

# Islands (Dec) -2.95 0.75 -0.02 -0.16

# Islands (Jan) -1.74 0.62 0.60 -0.76

# Islands (Feb) -1.80 -0.73 -0.54 -0.34

# Islands (Mar) -1.62 -1.16 -0.54 -0.29

# Islands (Apr) -0.21 0.05 0.40 0.12

Island Area -490.48 1962.36

Island Area (Oct) 831.79

Island Area (Nov) 2997.26

Island Area (Dec) 1142.16

Island Area (Jan) 373.82

Island Area (Feb) 522.26

Island Area (Mar) 1287.97

Island Area (Apr) 3152.42

Area Ratio 13.49

Distance to Island -1.37 -0.99

Distance to Island (Oct) 0.80 -0.35

Distance to Island (Nov) -0.26 -2.35

Distance to Island (Dec) -0.31 -1.25

Distance to Island (Jan) -1.10 -1.53

Distance to Island (Feb) -0.69 0.40

Distance to Island (Mar) -0.84 -1.53

Distance to Island (Apr) -0.28 -2.22

# Islands x Area -496.23 -106.87

# Islands x Area Ratio -9.36 a

Positive and negative parameter estimate values reflect the direction of the relationship between avian abundance and the variable of interest.

b

Island variables followed by month refer to the interaction between the variable and the particular month.
84

Table 23. Parameter estimatesa of island variables from the top-ranked models of roosting bird abundance at the grid scale.

Island

variableb

American 

avocets

Black-necked 

stilts

Western 

sandpipers

Dabbling 

ducks

Diving 

ducks

Small 

shorebirds

Medium 

shorebirds Piscivores Gulls Herons

HasIsland 1.98 2.24 1.18 1.015

HasIsland (Oct) 3.05 1.23 1.38 2.19

HasIsland (Nov) 2.62 -2.13 1.30 0.85

HasIsland (Dec) 1.30 -0.67 0.28 0.62

HasIsland (Jan) 1.80 1.74 0.44 0.79

HasIsland (Feb) -11.89 -2.67 0.96 0.96

HasIsland (Mar) 1.29 0.84 0.37 1.36

HasIsland (Apr) 0.08 4.10 -0.57 2.92

Number Islands

# Islands (Oct) -0.69

# Islands (Nov) -0.59

# Islands (Dec) -0.58

# Islands (Jan) 0.09

# Islands (Feb) 0.01

# Islands (Mar) -0.85

# Islands (Apr) -0.35

Island Area (Oct) 7649.87

Island Area (Nov) 255.52

Island Area (Dec) 1004.11

Island Area (Jan) -89.46

Island Area (Feb) 256.01

Island Area (Mar) 539.58

Island Area (Apr) -312.13

Distance to Island (Oct) -0.89

Distance to Island (Nov) -0.90

Distance to Island (Dec) -1.36

Distance to Island (Jan) -0.55

Distance to Island (Feb) -0.44

Distance to Island (Mar) -1.29

Distance to Island (Apr) -2.06 a

Positive and negative parameter estimate values reflect the direction of the relationship between avian abundance and the variable of interest.

b

Island variables followed by month refer to the interaction between the variable and the particular month.
85

Table 24. The influence of island variables on the foraging abundance of birds in grids.

Species or guild Presence of 

islandsa

Number of 

islands

Island 

area

Island-area to pondarea RatioDistance 

to islandb

Temporal 

variationc

American avocets + 0 0 0 + 0

Black-necked stilts + - 0 + 0 0

Eared grebes - 0 - 0 0 0

Forster's terns + - + 0 0 +

Western sandpipers + + 0 0 0 +

Dabbling ducks + 0 0 0 0 +

Diving ducks + 0 0 0 + +

Gulls + 0 0 0 + +

Herons + 0 0 0 0 +

Medium shorebirds + 0 0 0 + 0

Piscivores + - 0 + 0 +

Small shorebirds + - + 0 0 + a

Positive values were included for all birds with any positive island variable influence. b

Positive value indicates that bird abundance is higher in grids closer to islands. c

Positive value indicates that a temporal variation exists and does not refer to the direction of the island effect. The direction 

of influence is reported in table 22.

Table 25. The influence of island variables on the roosting abundance of birds in grids.

Species or guild Presence of 

islandsa

Number of 

islands

Island 

area

Island-area to 

Pond-area ratio

Distance 

to islandb

Temporal 

variationc

American avocets + 0 0 0 0 0

Black-necked stilts + 0 0 0 0 +

Eared grebes 0 0 0 0 0 0

Forster's terns 0 0 0 0 0 0

Western sandpipers + 0 0 0 0 +

Dabbling ducks + 0 0 0 0 +

Diving ducks + - + 0 0 +

Gulls 0 0 0 0 + +

Herons + 0 0 0 0 0

Medium shorebirds + 0 0 0 0 0

Piscivores + 0 0 0 0 0

Small shorebirds + 0 0 0 0 + a

Positive value indicates that bird abundance is higher in grids closer to islands.

b

Positive value indicates that a temporal variation exists and does not refer to the direction of the island effect. The direction 

of influence is reported in table 23.
86

Appendix 1. AIC Tables for Pond and Grid Scale Analyses of the Importance of 

Islands for Foraging and Roosting Waterbirds

Table A1. The ranking of candidate models at the pond scale for foraging and roosting American avocets in the 

former salt production ponds of South San Francisco Bay, California. 

[Models are ranked by differences in Akaike’s information criterion and only models of ∆AIC less than 7 and the base model 

are presented] 

Model name k -2LogL AICc ∆AICc wi

Evidence 

ratio

Foraging

Base + #Island 17 9729.00 9763.26 0.00 0.36 1.00

Base + #Island + AreaRatio 18 9728.32 9764.61 1.35 0.18 1.96

Base + #Island + IslandArea 18 9728.74 9765.03 1.77 0.15 2.42

Base + HasIsland + Month × HasIsland 23 9719.94 9766.41 3.15 0.07 4.83

Base + #Island + AreaRatio + #Island ×

AreaRatio 19 9728.30 9766.62 3.36 0.07 5.37

Base + #Island + IslandArea + #Island ×

IslandArea 19 9728.64 9766.96 3.70 0.06 6.37

Base + #Island + #Island × Month 23 9721.76 9768.23 4.97 0.03 12.01

Base + HasIsland 17 9734.60 9768.86 5.60 0.02 16.44

Base + #Island + AreaRatio + #Island × Month 24 9721.12 9769.63 6.37 0.01 24.19

Base + #Island + IslandArea + IslandArea ×

Month 24 9721.36 9769.87 6.61 0.01 27.28

Base + #Island + IslandArea + #Island × Month 24 9721.54 9770.05 6.79 0.01 29.85

Base 16 9738.86 9771.09 7.83 0.01 50.17

Roosting

Base + #Island + AreaRatio + AreaRatio × Month 24 12941.20 12989.71 0.00 0.28 1.00

Base + #Island + IslandArea + IslandArea ×

Month 24 12942.00 12990.51 0.80 0.18 1.49

Base + #Island + #Island × Month 23 12944.32 12990.79 1.08 0.16 1.71

Base + #Island + AreaRatio + #Island × Month 24 12942.30 12990.81 1.10 0.16 1.73

Base + #Island + AreaRatio + #Island × 

AreaRatio + AreaRatio × Month

25 12940.44 12991.00 1.28 0.15 1.90

Base + HasIsland 17 12959.00 12993.26 3.55 0.05 5.89

Base + #Island + AreaRatio + #Island × 

AreaRatio + #Island × Month + AreaRatio × 

Month + #Island × AreaRatio × Month

37 12919.98 12995.19 5.48 0.02 15.45

Base + HasIsland + Month × HasIsland 23 12950.20 12996.67 6.96 0.01 32.44

Base 16 12974.84 13007.07 17.36 0.00 5880.71 a

Birds were surveyed across the former salt ponds during October through April of 2002 – 2013. Variables considered included the 

presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio (AreaRatio), and 

interactions between these variables and between these variables and month. b

Number of estimated parameters in the model.

c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model. 
87

Table A2. The ranking of candidate models at the pond scale for foraging and roosting black-necked stiltsa in the 

former salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICcc ∆AICc

d wie Evidence 

ratiof

Foraging

Base 16 6974.36 7006.59 0.00 0.23 1.00

Base + #Island 17 6972.86 7007.12 0.53 0.18 1.30

Base + HasIsland 17 6973.22 7007.48 0.89 0.15 1.56

Base + #Island + AreaRatio + #Island × AreaRatio 19 6969.54 7007.86 1.27 0.12 1.89

Base + #Island + IslandArea + #Island × IslandArea 19 6970.46 7008.78 2.19 0.08 2.99

Base + #Island + IslandArea 18 6972.80 7009.09 2.50 0.07 3.49

Base + #Island + AreaRatio 18 6972.84 7009.13 2.54 0.07 3.56

Base + HasIsland + Month × HasIsland 23 6964.50 7010.97 4.38 0.03 8.93

Base + #Island + #Island × Month 23 6965.16 7011.63 5.04 0.02 12.43

Base + #Island + AreaRatio + #Island × AreaRatio + 

#Island × Month

25 6961.76 7012.32 5.72 0.01 17.50

Base + #Island + IslandArea + #Island × IslandArea + 

#Island × Month

25 6962.86 7013.42 6.82 0.01 30.33

Base + #Island + IslandArea + #Island × Month 24 6965.06 7013.57 6.98 0.01 32.80

Roosting

Base + HasIsland 17 5106.44 5140.70 0.00 0.18 1.00

Base + #Island 17 5106.54 5140.80 0.10 0.17 1.05

Base + #Island + #Island × Month 23 5095.00 5141.47 0.77 0.12 1.47

Base 16 5109.50 5141.73 1.03 0.11 1.67

Base + #Island + AreaRatio 18 5106.52 5142.81 2.11 0.06 2.87

Base + #Island + IslandArea 18 5106.52 5142.81 2.11 0.06 2.87

Base + #Island + AreaRatio + AreaRatio × Month 24 5094.42 5142.93 2.23 0.06 3.05

Base + #Island + IslandArea + #Island × Month 24 5094.78 5143.29 2.59 0.05 3.65

Base + #Island + AreaRatio + #Island × Month 24 5094.92 5143.43 2.73 0.05 3.92

Base + #Island + IslandArea + #Island × IslandArea 19 5106.44 5144.76 4.06 0.02 7.63

Base + #Island + IslandArea + IslandArea × Month 24 5096.28 5144.79 4.09 0.02 7.74

Base + HasIsland + Month × HasIsland 23 5098.34 5144.81 4.11 0.02 7.81

Base + #Island + AreaRatio + #Island × AreaRatio 19 5106.52 5144.84 4.14 0.02 7.94

Base + #Island + AreaRatio + #Island × AreaRatio + 

AreaRatio × Month

25 5094.42 5144.98 4.28 0.02 8.48

Base + #Island + IslandArea + #Island × IslandArea + 

#Island × Month

25 5094.78 5145.34 4.64 0.02 10.15

Base + #Island + AreaRatio + #Island × AreaRatio + 

#Island × Month

25 5094.88 5145.44 4.74 0.02 10.67

Base + #Island + IslandArea + #Island × IslandArea + 

IslandArea × Month

25 5096.26 5146.82 6.12 0.01 21.27

a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included the presence or absence of 

islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio (AreaRatio), and interactions between these variables and 

between these variables and month. b

Number of estimated parameters in the model.

c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model. 
88

Table A3. The ranking of candidate models at the pond scale for foraging and roosting diving ducksa in the former 

salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICcc ∆AICcd wi

e Evidence 

ratiof

Foraging

Base + HasIsland + Month × HasIsland 23 18274.08 18320.55 0.00 0.17 1.00

Base + #Island + #Island × Month 23 18274.62 18321.09 0.54 0.13 1.31

Base + #Island + AreaRatio + #Island × 

AreaRatio + #Island × Month

25 18271.22 18321.78 1.22 0.09 1.84

Base + #Island + IslandArea + #Island × 

IslandArea + #Island × Month + IslandArea × 

Month + #Island × IslandArea × Month

37 18246.98 18322.19 1.64 0.08 2.27

Base + #Island + IslandArea + #Island × 

IslandArea + #Island × Month

25 18272.18 18322.74 2.18 0.06 2.98

Base 16 18290.62 18322.85 2.30 0.05 3.16

Base + #Island + IslandArea + #Island ×

IslandArea + #Island × Month + IslandArea × 

Month

31 18260.02 18322.87 2.32 0.05 3.19

Base + #Island + AreaRatio + #Island × Month 24 18274.48 18322.99 2.44 0.05 3.39

Base + #Island + IslandArea + #Island × Month 24 18274.62 18323.13 2.58 0.05 3.64

Base + #Island 17 18288.90 18323.16 2.61 0.05 3.69

Base + HasIsland 17 18288.94 18323.20 2.65 0.05 3.76

Base + #Island + IslandArea + #Island × Month 

+ IslandArea × Month

30 18262.56 18323.36 2.81 0.04 4.07

Base + #Island + AreaRatio + #Island ×

AreaRatio

19 18285.44 18323.76 3.21 0.03 4.98

Base + #Island + AreaRatio 18 18288.36 18324.65 4.10 0.02 7.77

Base + #Island + IslandArea + #Island ×

IslandArea

19 18286.48 18324.80 4.25 0.02 8.38

Base + #Island + IslandArea 18 18288.80 18325.09 4.54 0.02 9.68

Base + #Island + AreaRatio + #Island ×

AreaRatio + #Island × Month + AreaRatio × 

Month

31 18262.52 18325.37 4.82 0.02 11.13

Base + #Island + AreaRatio + #Island × Month 

+ AreaRatio × Month

30 18265.42 18326.22 5.67 0.01 16.99

Roosting

Base + HasIsland + Month × HasIsland 23 24068.40 24114.87 0.00 0.98 1.00

Base 16 24106.60 24138.83 23.96 0.00 159539

a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), and interactions between these variables and between these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
89

Table A4. The ranking of candidate models at the pond scale for foraging and roosting eared grebesa in the former 

salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICc

c ∆AICcd wie Evidence 

ratiof

Foraging

Base + HasIsland + Month × HasIsland 23 11387.40 11433.87 0.00 1.00 1.00

Base 16 11424.84 11457.07 23.20 0.00 109103

Roosting

Base 16 9965.36 9997.59 0.00 0.20 1.00

Base + HasIsland + Month × HasIsland 23 9951.30 9997.77 0.18 0.18 1.09

Base + #Island + AreaRatio 18 9961.86 9998.15 0.56 0.15 1.32

Base + #Island 17 9964.00 9998.26 0.67 0.14 1.40

Base + HasIsland 17 9964.92 9999.18 1.59 0.09 2.21

Base + #Island + AreaRatio + #Island ×

AreaRatio

19 9961.16 9999.48 1.89 0.08 2.58

Base + #Island + IslandArea 18 9963.44 9999.73 2.14 0.07 2.92

Base + #Island + IslandArea + #Island ×

IslandArea

19 9963.40 10001.72 4.13 0.02 7.89

Base + #Island + #Island × Month 23 9955.36 10001.83 4.24 0.02 8.33

Base + #Island + AreaRatio + #Island ×

Month

24 9953.40 10001.91 4.32 0.02 8.68

Base + #Island + AreaRatio + #Island × 

AreaRatio + #Island × Month

25 9952.60 10003.16 5.56 0.01 16.15

Base + #Island + IslandArea + #Island ×

Month

24 9954.92 10003.43 5.84 0.01 18.55

a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), and interactions between these variables and between these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
90

Table A5. The ranking of candidate models at the pond scale for foraging and roosting small shorebirdsa in the 

former salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICc

c ∆AICcd wie Evidence 

ratiof

Foraging

Base + HasIsland + Month × HasIsland 23 21311.40 21357.87 0.00 1.00 1.00

Base 16 21353.40 21385.63 27.76 0.00 1066664

Roosting

Base + HasIsland 17 21016.00 21050.26 0.00 0.77 1.00

Base + HasIsland + Month × HasIsland 23 21009.00 21055.47 5.21 0.06 13.54

Base + #Island + IslandArea + #Island ×

IslandArea

19 21017.20 21055.52 5.26 0.06 13.90

Base + #Island + AreaRatio + #Island ×

AreaRatio

19 21017.80 21056.12 5.86 0.04 18.76

Base 16 21024.80 21057.03 6.77 0.03 29.53

a

Birds were surveyed across the former salt ponds during October through April of 2002 – 2013. Variables considered 

included the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pondarea ratio (AreaRatio), and interactions between these variables and between these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
91

Table A6. The ranking of candidate models at the pond scale for foraging and roosting medium shorebirdsa in the 

former salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICc

c ∆AICcd wie Evidence 

ratiof

Foraging

Base + HasIsland + Month × HasIsland 23 15599.60 15646.07 0.00 0.68 1.00

Base + #Island + AreaRatio + #Island ×

AreaRatio

19 15611.84 15650.16 4.09 0.09 7.74

Base + #Island + IslandArea + #Island ×

IslandArea

19 15612.20 15650.52 4.45 0.07 9.27

Base + #Island 17 15616.74 15651.00 4.93 0.06 11.76

Base 16 15619.68 15651.91 5.84 0.04 18.54

Base + #Island + AreaRatio 18 15616.24 15652.53 6.46 0.03 25.28

Base + #Island + IslandArea 18 15616.72 15653.01 6.94 0.02 32.14

Roosting

Base + #Island + AreaRatio + #Island ×

AreaRatio

19 20700.60 20738.92 0.00 0.69 1.00

Base + #Island + IslandArea + #Island ×

IslandArea

19 20703.00 20741.32 2.40 0.21 3.32

Base + #Island + AreaRatio 18 20708.20 20744.49 5.57 0.04 16.18

Base + #Island 17 20711.60 20745.86 6.94 0.02 32.08

Base 16 20735.00 20767.23 28.31 0.00 1402541.30

a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), and interactions between these variables and between these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
92

Table A7. The ranking of candidate models at the pond scale for foraging and roosting Western sandpipersa in the 

former salt production ponds of South San Francisco Bay,California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICcc ∆AICcd wi

e Evidence 

ratiof

Foraging

Base + HasIsland + Month × HasIsland 23 14484.90 14531.37 0.00 0.76 1.00

Base + #Island + IslandArea + #Island ×

IslandArea + #Island × Month + IslandArea × 

Month + #Island × IslandArea × Month

37 14459.42 14534.63 3.26 0.15 5.10

Base + #Island + AreaRatio + #Island ×

AreaRatio + #Island × Month + AreaRatio × 

Month + #Island × AreaRatio × Month

37 14461.38 14536.59 5.22 0.06 13.58

Base + HasIsland 17 14504.08 14538.34 6.97 0.02 32.61

Base 16 14518.58 14550.81 19.44 0.00 16648.02

Roosting

Base + HasIsland + Month × HasIsland 23 13404.56 13451.03 0.00 0.90 1.00

Base + HasIsland 17 13422.74 13457.00 5.97 0.05 19.78

Base 16 13426.22 13458.45 7.42 0.02 40.86

a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), and interactions between these variables and between these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
93

Table A8. The ranking of candidate models at the pond scale for foraging and roosting dabbling ducksa in the 

former salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICcc ∆AICcd wi

e Evidence 

ratiof

Foraging

Base + HasIsland 17 20568.20 20602.46 0.00 0.25 1.00

Base + #Island + IslandArea + #Island ×

IslandArea

19 20564.60 20602.92 0.46 0.20 1.26

Base + #Island + AreaRatio + #Island ×

AreaRatio

19 20565.00 20603.32 0.86 0.16 1.54

Base + #Island 17 20569.60 20603.86 1.40 0.13 2.01

Base + HasIsland + Month × HasIsland 23 20558.00 20604.47 2.01 0.09 2.73

Base + #Island + AreaRatio 18 20569.40 20605.69 3.23 0.05 5.03

Base + #Island + IslandArea 18 20569.40 20605.69 3.23 0.05 5.03

Base + #Island + IslandArea + #Island × 

IslandArea + IslandArea × Month

25 20556.20 20606.76 4.30 0.03 8.56

Base + #Island + IslandArea + #Island × 

IslandArea + #Island × Month

25 20558.80 20609.36 6.90 0.01 31.42

Base 16 20601.40 20633.63 31.17 0.00 5870580

Roosting

Base + #Island + AreaRatio + #Island ×

AreaRatio

19 22903.20 22941.52 0.00 0.25 1.00

Base + #Island + IslandArea + #Island ×

IslandArea

19 22904.00 22942.32 0.80 0.17 1.49

Base + #Island + AreaRatio + #Island × 

AreaRatio + #Island × Month

25 22891.80 22942.36 0.83 0.17 1.52

Base + #Island + IslandArea + #Island × 

IslandArea + IslandArea × Month

25 22892.20 22942.76 1.23 0.14 1.85

Base + #Island + IslandArea + #Island × 

IslandArea + #Island × Month

25 22892.40 22942.96 1.43 0.12 2.05

Base + #Island + AreaRatio + #Island × 

AreaRatio + AreaRatio × Month

25 22895.00 22945.56 4.03 0.03 7.51

Base + #Island + AreaRatio 18 22909.80 22946.09 4.57 0.03 9.81

Base + #Island + AreaRatio + #Island ×

Month

24 22898.20 22946.71 5.19 0.02 13.39

Base + #Island + IslandArea + IslandArea ×

Month

24 22898.60 22947.11 5.59 0.02 16.35

Base + #Island + AreaRatio + AreaRatio ×

Month

24 22899.00 22947.51 5.99 0.01 19.97

Base + #Island 17 22913.80 22948.06 6.54 0.01 26.27

Base + #Island + #Island × Month 23 22901.60 22948.07 6.55 0.01 26.41

Base 16 22975.60 23007.83 66.31 0.00 2.5E+14 a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), and interactions between these variables and between these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
94

Table A9. The ranking of candidate models at the pond scale for foraging and roosting gullsa in the former salt 

production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s information 

criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICcc ∆AICcd wi

e Evidence 

ratiof

Foraging

Base + #Island + IslandArea + IslandArea ×

Month

24 8858.84 8907.35 0.00 0.61 1.00

Base + #Island + IslandArea + #Island × 

IslandArea + IslandArea × Month

25 8858.00 8908.56 1.20 0.33 1.82

Base 16 8900.28 8932.51 25.16 2.1E-06 290522

Roosting

Base + #Island + AreaRatio + #Island × 

AreaRatio + #Island × Month

25 20258.60 20309.16 0.00 0.87 1.00

Base + #Island + AreaRatio + #Island ×

AreaRatio

19 20275.20 20313.52 4.37 0.10 8.88

Base 16 20332.60 20364.83 55.68 7.1E-13 1.2E+12

a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), and interactions between these variables and between these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
95

Table A10. The ranking of candidate models at the pond scale for foraging and roosting piscivoresa in the former 

salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICcc ∆AICc

d wie Evidence 

ratiof

Foraging

Base + #Island + IslandArea + IslandArea × Month 24 11476.08 11524.59 0.00 0.28 1.00

Base + #Island + #Island × Month 23 11479.38 11525.85 1.26 0.15 1.88

Base + #Island + IslandArea + #Island × IslandArea + 

IslandArea × Month

25 11475.86 11526.42 1.82 0.11 2.49

Base + #Island + AreaRatio + #Island × Month 24 11478.28 11526.79 2.20 0.09 3.00

Base + #Island 17 11493.12 11527.38 2.79 0.07 4.03

Base + #Island + IslandArea + #Island × Month 24 11479.20 11527.71 3.12 0.06 4.76

Base + #Island + AreaRatio 18 11491.94 11528.23 3.64 0.05 6.17

Base + #Island + AreaRatio + #Island × AreaRatio + 

#Island × Month

25 11477.68 11528.24 3.64 0.05 6.18

Base + #Island + IslandArea 18 11492.84 11529.13 4.54 0.03 9.67

Base + #Island + IslandArea + #Island × IslandArea + 

#Island × Month

25 11479.12 11529.68 5.08 0.02 12.70

Base + #Island + AreaRatio + #Island × AreaRatio 19 11491.66 11529.98 5.39 0.02 14.81

Base + HasIsland 17 11496.50 11530.76 6.17 0.01 21.84

Base + #Island + AreaRatio + AreaRatio × Month 24 11482.52 11531.03 6.44 0.01 25.03

Base + #Island + IslandArea + #Island × IslandArea 19 11492.82 11531.14 6.55 0.01 26.46

Base 16 11504.96 11537.19 12.60 0.00 544.26

Roosting

Base + #Island + AreaRatio 18 14693.06 14729.35 0.00 0.19 1.00

Base + #Island + AreaRatio + #Island × AreaRatio 19 14691.04 14729.36 0.01 0.18 1.01

Base + #Island 17 14695.58 14729.84 0.49 0.15 1.28

Base + #Island + AreaRatio + #Island × AreaRatio + 

#Island × Month

25 14679.78 14730.34 0.98 0.11 1.64

Base + #Island + AreaRatio + #Island × Month 24 14682.06 14730.57 1.22 0.10 1.84

Base + #Island + #Island × Month 23 14684.60 14731.07 1.72 0.08 2.36

Base + #Island + IslandArea 18 14695.16 14731.45 2.10 0.07 2.86

Base + #Island + IslandArea + #Island × IslandArea 19 14694.02 14732.34 2.99 0.04 4.46

Base + #Island + IslandArea + #Island × Month 24 14684.18 14732.69 3.34 0.03 5.32

Base + #Island + IslandArea + #Island:IslandArea + 

#Island × Month

25 14682.84 14733.40 4.04 0.02 7.55

Base + #Island + AreaRatio + #Island × AreaRatio + 

AreaRatio × Month

25 14685.56 14736.12 6.76 0.01 29.43

Base 16 14727.62 14759.85 30.50 0.00 4197767

a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included the presence or 

absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio (AreaRatio), and interactions 

between these variables and between these variables and month. b

Number of estimated parameters in the model.

c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
96

Table A11. The ranking of candidate models at the pond scale for foraging and roosting Forster’s ternsa in the 

former salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICc

c ∆AICcd wie Evidence 

ratiof

Foraging

Base + #Island + IslandArea + #Island × 

IslandArea + #Island × Month + IslandArea × 

Month + #Island × IslandArea × Month

37 4268.02 4343.23 0.00 0.85 1.00

Base + #Island + AreaRatio + #Island × 

AreaRatio + #Island × Month + AreaRatio × 

Month + #Island × AreaRatio × Month

37 4272.06 4347.27 4.04 0.11 7.54

Base 16 4327.82 4360.05 16.82 0.00 4499

Roosting

Base + #Island + AreaRatio + #Island × 

AreaRatio + #Island × Month

25 5492.40 5542.96 0.00 0.34 1.00

Base + HasIsland + Month × HasIsland 23 5497.20 5543.67 0.72 0.24 1.43

Base + HasIsland 17 5510.32 5544.58 1.62 0.15 2.25

Base + #Island + IslandArea + #Island × 

IslandArea + #Island × Month

25 5494.58 5545.14 2.18 0.11 2.97

Base + #Island + #Island × Month 23 5499.92 5546.39 3.44 0.06 5.57

Base + #Island + AreaRatio + #Island × Month 24 5499.30 5547.81 4.86 0.03 11.34

Base + #Island + IslandArea + #Island × Month 24 5499.92 5548.43 5.48 0.02 15.46

Base 16 5519.16 5551.39 8.44 0.01 67.89

a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), and interactions between these variables and between these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
97

Table A12. The ranking of candidate models at the pond scale for foraging and roosting heronsa in the former salt 

production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s information 

criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICcc ∆AICcd wi

e Evidence ratiof

Foraging

Base + #Island + AreaRatio 18 9071.62 9107.91 0.00 0.39 1.00

Base + #Island 17 9075.00 9109.26 1.35 0.20 1.96

Base + #Island + AreaRatio + #Island ×

AreaRatio

19 9071.24 9109.56 1.65 0.17 2.28

Base + #Island + IslandArea 18 9073.34 9109.63 1.72 0.16 2.36

Base + #Island + IslandArea + #Island ×

IslandArea

19 9073.18 9111.50 3.59 0.06 6.03

Base 16 9105.78 9138.01 30.10 0.00 3436841

Roosting

Base + #Island + AreaRatio + #Island ×

AreaRatio

19 9420.86 9459.18 0.00 0.58 1.00

Base + #Island + AreaRatio 18 9425.18 9461.47 2.29 0.18 3.14

Base + #Island + AreaRatio + #Island ×

AreaRatio + AreaRatio × Month

25 9412.96 9463.52 4.33 0.07 8.72

Base + #Island + AreaRatio + #Island ×

AreaRatio + #Island × Month

25 9413.42 9463.98 4.79 0.05 10.98

Base + #Island + IslandArea + #Island ×

IslandArea

19 9426.58 9464.90 5.72 0.03 17.46

Base + #Island 17 9430.78 9465.04 5.86 0.03 18.70

Base + #Island + IslandArea 18 9429.24 9465.53 6.35 0.02 23.90

Base 16 9465.76 9497.99 38.81 0.00 2.67E+08

a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), and interactions between these variables and between these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
98

Table A13. The ranking of candidate models at the grid scale for foraging and roosting diving ducksa in the former 

salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICc

c ∆AICcd wie Evidence 

ratiof

Foraging

Base + IslandDist + Month × IslandDist 25 16070.06 16120.24 0.00 1.00 1.00

Base 18 16119.06 16155.15 34.92 0.00 38181056

Roosting

Base + #Island + IslandArea + #Island ×

IslandArea + #Island × Month + IslandArea ×

Month + #Island × IslandArea × Month

39 27964.60 28043.03 0.00 0.68 1.00

Base + #Island + AreaRatio + #Island ×

AreaRatio + #Island × Month + AreaRatio ×

Month + #Island × AreaRatio × Month

39 27966.60 28045.03 2.00 0.25 2.72

Base + #Island + IslandArea + #Island ×

Month + IslandArea × Month

32 27984.80 28049.09 6.06 0.03 20.71

Base 18 28037.80 28073.89 30.87 0.00 5040618 a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), and interactions between these variables and between these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.

Table A14. The ranking of candidate models at the grid scale for foraging and roosting gullsa in the former salt 

production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s information 

criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICcc ∆AICcd wi

e Evidence ratiof

Foraging

Base + IslandDist + Month ×

IslandDist 25 4924.12 4974.30 0.00 0.99 1.00

Base 18 4961.88 4997.97 23.68 0.00 138392.63

Roosting

Base + IslandDist + Month ×

IslandDist 25 16142.88 16193.06 0.00 0.99 1.00

Base 18 16177.16 16213.25 20.20 0.00 24290.73 a

Birds were surveyed across the former salt ponds during October through April of 2002 – 2013. Variables considered 

included the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pondarea ratio (AreaRatio), and interactions between these variables and between these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
99

Table A15. The ranking of candidate models at the grid scale for foraging and roosting medium shorebirdsa in the 

former salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICcc ∆AICcd wi

e Evidence 

ratiof

Foraging

Base + IslandDist + Month × IslandDist 25 18752.58 18802.76 0.00 0.39 1.00

Base + HasIsland + Month × HasIsland 25 18753.10 18803.28 0.52 0.30 1.30

Base + IslandDist 19 18765.40 18803.50 0.75 0.27 1.45

Base + HasIsland 19 18769.88 18807.98 5.23 0.03 13.64

Base 18 18784.88 18820.97 18.22 0.00 9025.87

Roosting

Base + HasIsland 19 21922.40 21960.50 0.00 0.93 1.00

Base + #Island + AreaRatio + #Island ×

AreaRatio 21 21924.40 21966.53 6.02 0.05 20.31

Base 18 21953.20 21989.29 28.79 0.00 1784775 a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), and interactions between these variables and between these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
100

Table A16. The ranking of candidate models at the grid scale for foraging and roosting American avocetsa in the 

former salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICcc ∆AICcd wi

e Evidence ratiof

Foraging

Base + IslandDist 19 10437.92 10476.02 0.00 0.65 1.00

Base+ IslandDist + Month × IslandDist 25 10428.10 10478.28 2.25 0.21 3.09

Base + HasIsland 19 10441.62 10479.72 3.70 0.10 6.36

Base + HasIsland + Month × HasIsland 25 10433.14 10483.32 7.29 0.02 38.36

Base 18 10452.80 10488.89 12.87 0.00 623.16

Roosting

Base + HasIsland 19 13234.04 13272.14 0.00 0.55 1.00

Base + IslandDist 19 13234.56 13272.66 0.52 0.42 1.30

Base + IslandDist + Month ×

IslandDist 25 13228.78 13278.96 6.81 0.02 30.17

Base 18 13252.52 13288.61 16.47 0.00 3769.90 a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), and interactions between these variables and between these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
101

Table A17. The ranking of candidate models at the grid scale for foraging and roosting black-necked stiltsa in the 

former salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICc

c ∆AICcd wie

Evidence 

ratiof

Foraging

Base + #Island + AreaRatio + #Island ×

AreaRatio 21 5669.70 5711.83 0.00 0.31 1.00

Base + HasIsland 19 5673.98 5712.08 0.26 0.27 1.14

Base + #Island + IslandArea + #Island ×

IslandArea 21 5670.36 5712.49 0.66 0.22 1.39

Base + HasIsland + Month × HasIsland 25 5664.86 5715.04 3.21 0.06 4.98

Base + #Island + AreaRatio + #Island ×

AreaRatio + #Island × Month

27 5661.80 5716.01 4.18 0.04 8.09

Base + #Island + IslandArea + #Island ×

IslandArea + #Island × Month 27 5661.86 5716.07 4.24 0.04 8.33

Base + #Island + IslandArea + #Island ×

IslandArea + #Island × Month + IslandArea

× Month

33 5650.60 5716.91 5.08 0.02 12.69

Base 18 5686.50 5722.59 10.77 0.00 217.80

Roosting

Base + HasIsland + Month × HasIsland 25 3037.38 3087.56 0.00 0.63 1.00

Base + IslandDist 19 3051.98 3090.08 2.53 0.18 3.54

Base + HasIsland 19 3053.54 3091.64 4.09 0.08 7.71

Base + IslandDist + Month × IslandDist 25 3042.72 3092.90 5.34 0.04 14.44

Base 18 3061.02 3097.11 9.56 0.01 118.85 a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), distance from the grid to the nearest island (IslandDist), and interactions between these variables and between 

these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
102

Table A18. The ranking of candidate models at the grid scale for foraging and roosting piscivoresa in the former 

salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICcc ∆AICcd wi

e

Evidence 

ratiof

Foraging

Base + #Island + AreaRatio + #Island × Month 26 9462.26 9514.45 0.00 0.23 1.00

Base + #Island + IslandArea + #Island ×

IslandArea + #Island × Month

27 9460.46 9514.67 0.21 0.21 1.11

Base + #Island + AreaRatio + #Island ×

AreaRatio + #Island × Month

27 9460.88 9515.09 0.63 0.17 1.37

Base + #Island + AreaRatio + AreaRatio ×

Month

26 9463.46 9515.65 1.20 0.13 1.82

Base + #Island + AreaRatio + #Island ×

AreaRatio + AreaRatio × Month

27 9461.98 9516.19 1.73 0.10 2.38

Base + #Island + AreaRatio + #Island × Month 

+ AreaRatio × Month

32 9453.06 9517.35 2.90 0.05 4.26

Base + #Island + AreaRatio + #Island ×

AreaRatio + #Island × Month + AreaRatio ×

Month

33 9451.54 9517.85 3.40 0.04 5.46

Base + #Island + IslandArea + #Island ×

IslandArea + #Island × Month + IslandArea ×

Month

33 9452.68 9518.99 4.54 0.02 9.66

Base + HasIsland + Month × HasIsland 25 9470.04 9520.22 5.77 0.01 17.87

Base + #Island + IslandArea + #Island ×

IslandArea + IslandArea × Month

27 9466.34 9520.55 6.09 0.01 21.06

Base + #Island + IslandArea + #Island × Month 26 9469.14 9521.33 6.88 0.01 31.19

Base 18 9501.80 9537.89 23.44 0.00 123097

Roosting

Base + HasIsland 19 12551.28 12589.38 0.00 0.65 1.00

Base + IslandDist 19 12554.52 12592.62 3.24 0.13 5.05

Base + HasIsland + Month × HasIsland 25 12542.56 12592.74 3.35 0.12 5.35

Base + IslandDist + Month × IslandDist 25 12543.52 12593.70 4.31 0.08 8.64

Base 18 12564.90 12600.99 11.61 0.00 331.89

a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), distance from the grid to the nearest island (IslandDist), and interactions between these variables and between 

these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
103

Table A19. The ranking of candidate models at the grid scale for foraging and roosting dabbling ducksa in the 

former salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICcc ∆AICcd wi

e Evidence ratiof

Foraging

Base + HasIsland + Month ×

HasIsland 25 29246.60 29296.78 0.00 0.72 1.00

Base + IslandDist + Month ×

IslandDist 25 29250.20 29300.38 3.60 0.12 6.05

Base + IslandDist 19 29265.20 29303.30 6.53 0.03 26.13

Base + HasIsland 19 29265.40 29303.50 6.73 0.02 28.88

Base 18 29268.60 29304.69 7.92 0.01 52.34

Roosting

Base + HasIsland + Month ×

HasIsland 25 35283.00 35333.18 0.00 0.98 1.00

Base 18 35328.20 35364.29 31.12 0.00 5710687.86 a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), distance from the grid to the nearest island (IslandDist), and interactions between these variables and between 

these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
104

Table A20. The ranking of candidate models at the grid scale for foraging and roosting herons and egretsa in the 

former salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICcc ∆AICcd wi

e

Evidence 

ratiof

Foraging

Base + HasIsland + Month × HasIsland 25 9891.06 9941.24 0.00 0.39 1.00

Base + #Island + IslandArea + #Island ×

IslandArea + #Island × Month

27 9889.62 9943.83 2.59 0.11 3.65

Base + #Island + IslandArea + #Island ×

IslandArea 21 9901.78 9943.91 2.67 0.10 3.80

Base + #Island + AreaRatio 20 9904.92 9945.03 3.80 0.06 6.68

Base + #Island + AreaRatio + #Island × Month 26 9892.90 9945.09 3.85 0.06 6.87

Base + #Island + AreaRatio + AreaRatio ×

Month 26 9893.16 9945.35 4.11 0.05 7.82

Base + #Island + AreaRatio + #Island ×

AreaRatio 21 9903.70 9945.83 4.59 0.04 9.92

Base + #Island + AreaRatio + #Island ×

AreaRatio + #Island × Month

27 9891.82 9946.03 4.79 0.04 10.96

Base + #Island + IslandArea + #Island ×

IslandArea + IslandArea × Month

27 9892.02 9946.23 4.99 0.03 12.12

Base + #Island + AreaRatio + #Island ×

AreaRatio + AreaRatio × Month

27 9892.06 9946.27 5.03 0.03 12.36

Base + IslandDist + Month × IslandDist 25 9896.98 9947.16 5.92 0.02 19.30

Base + #Island + IslandArea + #Island × Month 26 9895.54 9947.73 6.49 0.02 25.72

Base 18 9915.16 9951.25 10.02 0.00 149.58

Roosting

Base + HasIsland 19 7302.18 7340.28 0.00 0.53 1.00

Base + IslandDist + Month × IslandDist 25 7290.92 7341.10 0.81 0.35 1.50

Base + HasIsland + Month × HasIsland 25 7293.92 7344.10 3.81 0.08 6.73

Base 18 7322.54 7358.63 18.35 0.00 9650.87 a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), distance from the grid to the nearest island (IslandDist), and interactions between these variables and between 

these variables and month. b

Number of estimated parameters in the model. c Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
105

Table A21. The ranking of candidate models at the grid scale for foraging and roosting eared grebesa in the former 

salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICc

c ∆AICcd wie

Evidence 

ratiof

Foraging

Base + #Island + IslandArea 20 5129.94 5170.05 0.00 0.34 1.00

Base + #Island + IslandArea + #Island ×

IslandArea 21 5129.90 5172.03 1.97 0.13 2.68

Base 18 5136.02 5172.11 2.06 0.12 2.80

Base + IslandDist + Month × IslandDist 25 5122.10 5172.28 2.22 0.11 3.04

Base + #Island + AreaRatio 20 5133.24 5173.35 3.30 0.07 5.21

Base + #Island + AreaRatio + #Island ×

AreaRatio 21 5131.50 5173.63 3.57 0.06 5.96

Base + IslandDist 19 5135.84 5173.94 3.89 0.05 6.99

Base + #Island 19 5135.98 5174.08 4.03 0.05 7.50

Base + HasIsland 19 5136.00 5174.10 4.05 0.05 7.57

Base + #Island + IslandArea + IslandArea ×

Month 26 5124.34 5176.53 6.48 0.01 25.50

Roosting

Base + IslandDist + Month × IslandDist 25 3472.86 3523.04 0.00 0.68 1.00

Base + IslandDist 19 3487.68 3525.78 2.75 0.17 3.95

Base 18 3491.98 3528.07 5.04 0.06 12.40

Base + HasIsland 19 3491.12 3529.22 6.19 0.03 22.04

Base + #Island 19 3491.64 3529.74 6.71 0.02 28.59 a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), distance from the grid to the nearest island (IslandDist), and interactions between these variables and between 

these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
106

Table A22. The ranking of candidate models at the grid scale for foraging and roosting Forster’s ternsa in the 

former salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model name kb -2LogL AICc

c ∆AICcd wie

Evidence 

ratiof

Foraging

Base + #Island + IslandArea + #Island ×

IslandArea + #Island × Month

27 1893.75 1947.95 0.00 0.46 1.00

Base + #Island + IslandArea + #Island ×

IslandArea + IslandArea × Month

27 1895.46 1949.67 1.72 0.19 2.36

Base + #Island + AreaRatio + #Island ×

AreaRatio + #Island × Month

27 1896.51 1950.72 2.77 0.11 3.99

Base + #Island + AreaRatio + #Island ×

AreaRatio + AreaRatio × Month

27 1898.00 1952.20 4.25 0.05 8.37

Base + #Island + AreaRatio + #Island × Month 26 1900.35 1952.54 4.59 0.05 9.90

Base + #Island + IslandArea + #Island × Month 26 1900.60 1952.79 4.84 0.04 11.23

Base + #Island + AreaRatio + AreaRatio ×

Month

26 1900.63 1952.82 4.87 0.04 11.39

Base 18 1925.96 1962.05 14.10 0.00 1150.94

Roosting

Base + HasIsland + Month × HasIsland 25 3656.34 3706.52 0.00 0.42 1.00

Base + #Island + #Island × Month 25 3657.48 3707.66 1.14 0.24 1.77

Base + #Island + AreaRatio + #Island × Month 26 3656.94 3709.13 2.61 0.11 3.70

Base + #Island + IslandArea + #Island × Month 26 3657.00 3709.19 2.67 0.11 3.81

Base + #Island + AreaRatio + #Island ×

AreaRatio + #Island × Month

27 3656.58 3710.79 4.27 0.05 8.45

Base + #Island + IslandArea + Island × Month 

+ IslandArea × Month

32 3647.18 3711.47 4.95 0.03 11.89

Base + #Island + IslandArea + #Island ×

IslandArea + #Island × Month + IslandArea ×

Month

33 3646.26 3712.57 6.05 0.02 20.59

Base 18 3684.98 3721.07 14.56 0.00 1447.87

a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), distance from the grid to the nearest island (IslandDist), and interactions between these variables and between 

these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
107

Table A23. The ranking of candidate models at the grid scale for foraging and roosting small shorebirdsa in the 

former salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model Name kb -2LogL AICcc ∆AICcd wi

e

evidence 

ratiof

Foraging

Base + #Island + IslandArea + #Island × IslandArea + 

#Island × Month + IslandArea × Month

33 28951.00 29017.31 0.00 0.38 1.00

Base + #Island + IslandArea + #Island × IslandArea + 

IslandArea × Month

27 28965.00 29019.21 1.90 0.15 2.59

Base + IslandDist + Month × IslandDist 25 28969.40 29019.58 2.27 0.12 3.11

Base + HasIsland 19 28982.20 29020.30 3.00 0.09 4.47

Base + #Island + AreaRatio + #Island × AreaRatio + 

AreaRatio × Month

27 28967.00 29021.21 3.90 0.05 7.03

Base + #Island + IslandArea + #Island × IslandArea + 

#Island × Month

27 28967.60 29021.81 4.50 0.04 9.49

Base + #Island + IslandArea + #Island × Month + 

IslandArea × Month

32 28957.60 29021.89 4.58 0.04 9.88

Base + #Island + AreaRatio + #Island × AreaRatio + 

#Island × Month

27 28968.00 29022.21 4.90 0.03 11.59

Base + #Island + AreaRatio + #Island × AreaRatio + 

#Island × Month + AreaRatio × Month

33 28957.00 29023.31 6.00 0.02 20.09

Base + #Island + IslandArea + #Island × IslandArea + 

#Island × Month + IslandArea × Month + #Island ×

IslandArea × Month

39 28945.60 29024.03 6.72 0.01 28.79

Base 18 28992.60 29028.69 11.39 0.00 296.83

Roosting

Base + HasIsland + Month × HasIsland 25 20649.20 20699.38 0.00 0.29 1.00

Base + #Island + AreaRatio + #Island × AreaRatio + 

#Island × Month

27 20645.20 20699.41 0.03 0.29 1.01

Base + #Island + IslandArea + #Island × IslandArea + 

#Island × Month

27 20645.80 20700.01 0.63 0.21 1.37

Base + #Island + AreaRatio + #Island × Month 26 20649.80 20701.99 2.61 0.08 3.70

Base + #Island + #Island × Month 25 20653.40 20703.58 4.20 0.04 8.17

Base + HasIsland 19 20666.40 20704.50 5.13 0.02 12.98

Base + #Island + IslandArea + #Island × Month 26 20652.80 20704.99 5.61 0.02 16.56

Base + #Island + AreaRatio + #Island × AreaRatio + 

AreaRatio × Month

27 20651.00 20705.21 5.83 0.02 18.44

Base + AreaRatio + #Island × AreaRatio + #Island ×

Month + AreaRatio × Month

33 20640.00 20706.31 6.93 0.01 31.97

Base 18 20678.60 20714.69 15.32 0.00 2117.20

a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included the presence or absence of 

islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio (AreaRatio), distance from the grid to the nearest island 

(IslandDist), and interactions between these variables and between these variables and month. b

Number of estimated parameters in the model.

c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
108

Table A24. The ranking of candidate models at the grid scale for foraging and roosting Western sandpipersa in the 

former salt production ponds of South San Francisco Bay, California. Models are ranked by differences in Akaike’s 

information criterion and only models of ∆AIC less than 7 and the base model are presented.

Model Name kb -2LogL AICcc ∆AICcd wi

e evidence 

ratiof

Foraging

Base + #Island + IslandArea + #Island ×

IslandArea + #Island × Month + IslandArea ×

Month + #Island × IslandArea × Month

39 18011.96 18090.39 0.00 0.70 1.00

Base + #Island + IslandArea + #Island ×

IslandArea + #Island × Month + IslandArea ×

Month

33 18027.50 18093.81 3.42 0.13 5.53

Base + #Island + IslandArea + #Island ×

IslandArea + IslandArea × Month

27 18039.88 18094.09 3.70 0.11 6.36

Base + #Island + AreaRatio + #Island ×

AreaRatio + #Island × Month + AreaRatio ×

Month + #Island × AreaRatio × Month

39 18017.70 18096.13 5.74 0.04 17.64

Base 18 18078.16 18114.25 23.87 0.00 152213

Roosting

Base + HasIsland + Month × HasIsland 25 11488.60 11538.78 0.00 0.51 1.00

Base + #Island + IslandArea + #Island × Month 

+ IslandArea × Month

32 11476.10 11540.39 1.61 0.23 2.24

Base + #Island + IslandArea + #Island ×

IslandArea + #Island × Month + IslandArea ×

Month

33 11474.64 11540.95 2.17 0.17 2.96

Base + #Island + IslandArea + #Island ×

IslandArea + #Island × Month + IslandArea ×

Month + #Island × IslandArea × Month

39 11464.60 11543.03 4.25 0.06 8.37

Base 18 11523.16 11559.25 20.48 0.00 27941

a

Birds were surveyed across the former salt ponds during October through April of 2002–13. Variables considered included 

the presence or absence of islands (HasIsland), the number of islands (#Island), island area, island-area-to-pond-area ratio

(AreaRatio), distance from the grid to the nearest island (IslandDist), and interactions between these variables and between 

these variables and month. b

Number of estimated parameters in the model. c

Second-order Akaike’s information criterion. d

The difference between the AICc of the current model and the AICc of the top model. e

Akaike weight – likelihood of the model relative to candidate models. f

The weight of evidence that the current model is inferior to the top model.
Publishing support provided by the U.S. Geological Survey

Publishing Network, Tacoma Publishing Service Center

For more information concerning the research in this report, contact the

 Director, Western Ecological Research Center

U.S. Geological Survey

3020 State University Drive East

Sacramento, California 95819 

http://werc.usgs.gov/
Ackerman and others—The Critical Role of Islands for Waterbird Breeding and Foraging Habitat in Managed Ponds of the South San Francisco Bay, California—Open-File Report 2014–1263

ISSN 2331-1258 (online)

http://dx.doi.org/10.3133/ofr20141263